Vehicle Detection Full Doc

8/12/2019 Vehicle Detection Full Doc

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Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks

Abstract

We present an automatic vehicle detection system for aerial surveillance inthis paper. In this system, we escape from the stereotype and existing frameworks

of vehicle detection in aerial surveillance, which are either region based or sliding

window based. We design a pixel wise classification method for vehicle detection.

The novelty lies in the fact that, in spite of performing pixel wise classification,

relations among neighboring pixels in a region are preserved in the feature

extraction process. We consider features including vehicle colors and local

features. For vehicle color extraction, we utilize a color transform to separate

vehicle colors and nonvehicle colors effectively. For edge detection, we apply

moment preserving to ad!ust the thresholds of the "anny edge detector

automatically, which increases the adaptability and the accuracy for detection in

various aerial images. #fterward, a dynamic $ayesian network %&$'( is

constructed for the classification purpose. We convert regional local features into

)uantitative observations that can be referenced when applying pixel wise

classification via &$'. *xperiments were conducted on a wide variety of aerial

videos. The results demonstrate flexibility and good generalization abilities of the

proposed method on a challenging data set with aerial surveillance images taken at

different heights and under different camera angles.


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Introduction:

#erial surveillance has a long history in the military for observing enemy activities

and in the commercial world for monitoring resources such as forests and crops. +imilar

imaging techni)ues are used in aerial news gathering and search and rescue aerial

surveillance has been performed primarily using film or electronic framing cameras. The

ob!ective has been to gather highresolution still images of an area under surveillance that

could later be examined by human or machine analysts to derive information of interest.

"urrently, there is growing interest in using video cameras for these tasks. ideo captures

dynamic events that cannot be understood from aerial still images. It enables feedback

and triggering of actions based on dynamic events and provides crucial and timely

intelligence and understanding that is not otherwise available. ideo observations can be

used to detect and geolocate moving ob!ects in real time and to control the camera, for

example, to follow detected vehicles or constantly monitor a site. -owever, video also

brings new technical challenges. ideo cameras have lower resolution than framing

cameras. In order to get the resolution re)uired to identify ob!ects on the ground, it is

generally necessary to use a telephoto lens, with a narrow field of view. This leads to the

most serious shortcoming of video in surveillance it provides only a /soda straw0 view

of the scene. The camera must then be scanned to cover extended regions of interest. #n

observer watching this video must pay constant attention, as ob!ects of interest move

rapidly in and out of the camera field of view. The video also lacks a larger visual context

the observer has difficulty perceiving the relative locations of ob!ects seen at one point

in time to ob!ects seen moments before. In addition, geodetic coordinates for ob!ects of

interest seen in the video are not available.

1ne of the main topics in aerial image analysis is scene registration and alignment.

#nother very important topic in intelligent aerial surveillance is vehicle detection and

tracking. The challenges of vehicle detection in aerial surveillance include camera

motions such as panning, tilting, and rotation. In addition, airborne platforms at different

heights result in different sizes of target ob!ects.


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In this paper, we design a new vehicle detection framework that preserves the

advantages of the existing works and avoids their drawbacks. The framework can be

divided into the training phase and the detection phase. In the training phase, we extract

multiple features including local edge and corner features, as well as vehicle colors totrain a dynamic $ayesian network %&$'(. In the detection phase, we first perform

background color removal. #fterward, the same feature extraction procedure is

performed as in the training phase. The extracted features serve as the evidence to infer

the unknown state of the trained &$', which indicates whether a pixel belongs to a

vehicle or not. In this paper, we do not perform regionbased classification, which would

highly depend on results of color segmentation algorithms such as mean shift. There is no

need to generate multiscale sliding windows either. The distinguishing feature of the

proposed framework is that the detection task is based on pixelwise classification.

-owever, the features are extracted in a neighborhood region of each pixel. Therefore,

the extracted features comprise not only pixellevel information but also relationship

among neighboring pixels in a region. +uch design is more effective and efficient than

regionbased or multiscale sliding window detection methods.


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Literature Survey

Vehicle Detection in Aerial Images using eneric !eatures" rou#ing and $onte%t

This work introduces a new approach on automatic vehicle detection in monocularlarge scale aerial images. The extraction is based on a hierarchical model that describes

the prominent vehicle features on different levels of detail. $esides the ob!ect properties,

the model comprises also contextual knowledge, i.e., relations between a vehicle and

other ob!ects as, e.g., the pavement beside a vehicle and the sun causing a vehicle2s

shadow pro!ection. This approach neither relies on external information like digital maps

or site models, nor is it limited to vial specific vehicle models.

Models

The knowledge about vehicle which is represented by the model is

summarized in Fig.3. &ue to the use of aerial imagery, i.e. almost vertical views,


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Extraction strategy

The extraction strategy is derived from the vehicle model and,

conse)uently, follows the two paradigms 9coarse to fine9 and 9hypothesize and

test9. It consists of following : steps; %3( "reation of


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parabolic surface. #s a side effect, both algorithms for line and surface extraction

return the contours of their bounding edges %bold black lines in Fig. 7 c(. These

contours and additional edges extracted in their neighborhood %thin white lines in

Fig. 7 c( are approximated by line segments. Then, we group the segments intorectangles and, furthermore, rectangle se)uences. Fitting the rectangle se)uences

to the dominant direction of the underlying image edges yields the final 7&

vehicle hypotheses %Fig. 7 d(.


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Hypotheses validation and selection

$efore constructing 5& models from the 7& hypotheses we check the

hypotheses for validity. *ach rectangle se)uence is evaluated regarding the

geometric criteria length, width, and length>width ratio, as well as the radiometric

criteria homogeneity of an individual rectangle and gray value constancy of

rectangles connected with a 9windshield rectangle9. The validation is based on

fuzzyset theory and ensures that only promising, non overlapping hypotheses are

selected %Fig. 7 e(.

3D model generation and verification

In order to approximate the 5& shape of a vehicle hypothesis, a particular

height profile is selected from a set of predefined profiles. 8lease note that the

hypothesis2 underlying rectangles remain unchanged, i.e., the height values of the

profile refer to the image edges perpendicular to the vehicle direction. The

selection of the profiles depends on the extracted sub structures, i.e., the shape of

the validated rectangle se)uence. We distinguish rectangle se)uences

corresponding to 5 types of vehicles; hatchback cars, saloon cars, and other

vehicles such as vans, small trucks, etc. In contrast to hatch back and saloon cars,

the derivation of an accurate height profile for the last category would re)uire a

deeper analysis of the hypotheses %e.g., for an unambiguous determination of the

vehicle orientation(. -ence, in this case, we approximate the height profile only

roughly by an elliptic arc having a constant height. offset, above the ground. #fter

creating a 5& model from the 7& hypothesis and the respective height profile we

are able to predict the boundary of a vehicle2s shadow pro!ection on the underlying

road surface. # vehicle hypothesis is !udged as verified if a dark and homogeneous

region is extracted besides the shadowed part of the vehicle and a bright and

homogeneous region besides the illuminated part, respectively %Fig. 7 f(.


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Vehicle Detection Using Normali&ed $olor and 'dge (a#

This work presents a novel vehicle detection approach for detecting vehicles from

static images using color and edges. &ifferent from traditional methods, which use

motion features to detect vehicles, this method introduces a new color transform model to

find important /vehicle color0 for )uickly locating possible vehicle candidates. +ince

vehicles have various colors under different weather and lighting conditions, seldom

works were proposed for the detection of vehicles using colors. The new color transform

model has excellent capabilities to identify vehicle pixels from background, even though

the pixels are lighted under varying illuminations. #fter finding possible vehiclecandidates, three important features, including corners, edge maps, and coefficients of

wavelet transforms, are used for constructing a cascade multichannel classifier.

#ccording to this classifier, an effective scanning can be performed to verify all possible

candidates )uickly. The scanning process can be )uickly achieved because most

background pixels are eliminated in advance by the color feature.

This work presents a novel system to detect vehicles from static images using

edges and vehicle colors. The flowchart of this system is shown in Fig. 5. #t the

beginning, a color transformation to pro!ect all the colors of input pixels on a color space

such that vehicle pixels can be easily identified from backgrounds. -ere, a $ayesian


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classifier is used for this identification. Then, each detected vehicle pixel will correspond

to a possible vehicle. +ince vehicles have different sizes and orientations, different

vehicle hypotheses are generated from each detected vehicle. For verifying each

hypothesis, we use three kinds of vehicle features to filter out all impossible vehiclecandidates. The features include edges, coefficients of wavelet transform, and corners.

?sing proper weights obtained from a set of training samples, these features can then be

combined together to form an optimal vehicle classifier. Then, desired vehicles can be

very robustly and accurately verified and detected from static images.

Detection o) Vehicles and Vehicle *ueues !or +oad (onitoring Using ,igh+esolution Aerial Images

This work introduces a new approach to automatic vehicle detection in monocular

high resolution aerial images. The extraction relies upon both local and global features of

vehicles and vehicle )ueues, respectively. To model a vehicle on local level, a 5&

wireframe representation is used that describes the prominent geometric and radiometric

features of cars including their shadow region. The model is adaptive because, during

extraction, the expected saliencies of various edge features are automatically ad!usted

depending on viewing angle, vehicle color measured from the image, and current

illumination direction. The extraction is carried out by matching this model 0topdown0

to the image and evaluating the support found in the image. 1n global level, the detailed

local description is extended by more generic knowledge about vehicles as they are often

part of vehicle queues. +uch groupings of vehicles are modeled by ribbons that exhibit

the typical symmetries and spacings of vehicles over a larger distance. @ueue extraction

includes the computation of directional edge symmetry measures resulting in a symmetry

map, in which dominant, smooth curvilinear structures are searched for. $y fusing

vehicles found using the local and the global model, the overall extraction gets more

complete and more correct.


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ehicle detection is carried out by a topdown matching algorithm. # comparison

with a grouping scheme that groups image features such as edges and homogeneous

regions into carlike structures has shown that matching the complete geometric model

topdown to the image is more robust. # reason for this is that, in general, bottomupgrouping needs reliable features as seed hypotheses which are hardly given in the case of

such small ob!ects like cars. #nother disadvantage of grouping refers to the fact that we

must constrain our detection algorithm to monocular images, since vehicles may move

within the time of two exposures. intensity at roof region

#dapt the expected saliency of the edge features depending on position,

orientation, color, and sun direction.

Aeasure features from the image; edge amplitude support of each model

edge, edge direction support of each model edge, color constancy, darkness

of shadow.

"ompute a matching score %a likelihood( by comparing measured values

with expected values.

$ased on the likelihood, decide whether the car hypothesis is accepted or

not.


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Vehicle Queue Detection

ehicle )ueue detection is based on searching for onevehicle wide ribbons

that are characterized by;

+ignificant directional symmetries of gray value edges withsymmetry maxima defining the )ueue4s center line.

Fre)uent intersections of short and perpendicularly oriented edges

with homogeneous distribution along the center line

-igh parallel edge support at both sides of the center line

+ufficient length

!ast Vehicle Detection and -racking In Aerial Image Bursts

This work present a combination of vehicle detection and tracking which is

adapted to the special restrictions given on image size and flow but nevertheless

yields reliable information about the traffic situation. "ombining a set of modified

edge filters it is possible to detect cars of different sizes and orientations with

minimum computing effort, if some a priori information about the street network is

used. The found vehicles are tracked between two consecutive images by an

algorithm using +ingular alue &ecomposition. "oncerning their distance and

correlation the features are assigned pair wise with respect to their global positioning

among each other. "hoosing only the best correlating assignments it is possible to

compute reliable values for the average velocities.

Preprocessing

To identify the active regions as well as the orientation of images among each

other they have to be georeferenced, which means their absolute geographic position

and dimension have to be defined. IA? information and a digital

terrain model the image data gets pro!ected into BeoTIFF images, which are plane

and oriented into north direction. This is useful to combine the recorded images with


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existing datasets like maps or street data. To avoid examining the whole image data,

only the street area given by a database is considered.

Detection

For providing fast detection of traffic ob!ects in the large images a set ofmodified edge filters, that represent a twodimensional car model, is used.


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orientation, ob!ects below a certain distance to each other are discarded while only the

one with the strongest intensity remains.

Vehicle Detection and -racking Using the Block (atching Algorithm

It describes an approach to vehicle detection and tracking fully based on the $lock

Aatching #lgorithm %$A#(, which is the motion estimation algorithm employed in

the A8*B compression standard. $A# partitions the current frame in small, fixed

size blocks and matches them in the previous frame in order to estimate blocks

displacement %referred to as motion vectors( between two successive frames. The

detection and tracking approach is as follows. $A# provides motion vectors, which

are then regularised using a ector Aedian Filter. #fter the regularisation step,

motion vectors are grouped based on their ad!acency and similarity, and a set of

vehicles is built per singular frame. Finally, the tracking algorithm establishes the

correspondences between the vehicles detected in each frames of the se)uence,

allowing the estimation of their tra!ectories as well as the detection of new entries and

exits. The tracking algorithm is strongly based on the $A#.

$A# consists in partitioning each frame of a given se)uence into s)uare blocks of

a fixed size %in pixel; CxC or DxD or...( and detecting blocks displacement between the

actual frame and the previous one, searching inside a given scan area. It provides a

field of displacement vectors %&F( associated with. *ach block encloses a part of the

image and is a matrix containing the grey tones of that image part. What we have to

do is estimating each block position in the previous frame in order to calculate

displacements and speeds %knowing Dt between frames(. *ach block defines in the

previous frame, a /scan area0, centered in the block center. The block is shifted pixel

bypixel inside the scan area, calculating a match measure at each shift position. The

comparison is aimed at determining the pixel set most similar to the block between its


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possible positions in the scan area. #mong these positions, the scan area subpart

defined by its center, will be the matrix with the best match measure.

Vehicle Detection and -racking )or the Urban $hallengeThe ?rban "hallenge 7==E was a race of autonomous vehicles through an urban

environment organized by the ?.+. government. &uring the competition the vehicles

encountered various typical scenarios of urban driving. -ere they had to interact with

other traffic which was human or machine driven. It describes the perception

approach taken by Team Tartan


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Vision.based detection" tracking and classi)ication o) vehicles using stable

)eatures with automatic camera calibration

# system for detection, tracking and classification of vehicles based on featurepoint tracking is presented in this chapter. #n overview of the system is shown in

Figure. Feature points are automatically detected and tracked through the video

se)uence, and features lying on the background or on shadows are removed by

background subtraction, leaving only features on the moving vehicles. These features

are then separated into two categories; stable and unstable. ?sing a plumb line

pro!ection %868(, the 5& coordinates of the stable features are computed, these stable

features are grouped together to provide a segmentation of the vehicles, and the

unstable features are then assigned to these groups. The final step involves

eliminating groups that do not appear to be vehicles, establishing correspondence

between groups detected in different image frames to achieve longterm tracking, and

classifying vehicles based upon the number of unstable features in the group. The

details of these steps are described in the following subsections.


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Vehicle Detection and -racking in $ar Video Based on (otion (odel

This work aims at realtime incar video analysis to detect and track vehicles

ahead for safety, autodriving, and target tracing. This paper describes acomprehensive approach to localize target vehicles in video under various

environmental conditions. The extracted geometry features from the video are

pro!ected onto a 3& profile continuously and are tracked constantly. We rely on

temporal information of features and their motion behaviors for vehicle identification,

which compensates for the complexity in recognizing vehicle shapes, colors, and

types. We model the motion in the field of view probabilistically according to the

scene characteristic and vehicle motion model. The -idden Aarkov Aodel is used for

separating target vehicles from background, and tracking them probabilistically


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'%isting System

-inz and $aumgartner utilized a hierarchical model that describes different levels

of details of vehicle features. There is no specific vehicle models assumed, making the

method flexible. -owever, their system would miss vehicles when the contrast is weak or

when the influences of neighboring ob!ects are present.

"heng and $utler considered multiple clues and used a mixture of experts to

merge the clues for vehicle detection in aerial images. They performed color

segmentation via meanshift algorithm and motion analysis via change detection. In

addition, they presented a trainable se)uential maximum a posterior method for

multiscale analysis and enforcement of contextual information. -owever, themotion

analysis algorithm applied in their system cannot deal with aforementioned camera

motions and complex background changes. Aoreover, in the information fusion step,

their algorithm highly depends on the color segmentation results.

6in et al. proposed a method by subtracting background colors of each frame and

then refined vehicle candidate regions by enforcing size constraints of vehicles. -owever,

they assumed too many parameters such as the largest and smallest sizes of vehicles, and

the height and the focus of the airborne camera. #ssuming these parameters as known

priors might not be realistic in real applications.

The authors proposed a movingvehicle detection method based on cascade

classifiers. # large number of positive and negative training samples need to be collected

for the training purpose. Aoreover, multiscale sliding windows are generated at the

detection stage. The main disadvantage of this method is that there are a lot of miss

detections on rotated vehicles. +uch results are not surprising from the experiences of

face detection using cascade classifiers. If only frontal faces are trained, then faces with

poses are easily missed. -owever, if faces with poses are added as positive samples, the

number of false alarms would surge.


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Disadvantage

-ierarchical model system would miss vehicles when the contrast is weak or when

the influences of neighboring ob!ects are present.

*xisting method result highly depends on the color segmentation

a lot of miss detections on rotated vehicles

a vehicle tends to be separated as many regions since car roofs and windshields

usually have different colors

high computational complexity

/ro#osed System

In this paper, we design a new vehicle detection framework that preserves the

advantages of the existing works and avoids their drawbacks. The framework can bedivided into the training phase and the detection phase. In the training phase, we extract

multiple features including local edge and corner features, as well as vehicle colors to

train a dynamic $ayesian network %&$'(. In the detection phase, we first perform

background color removal. #fterward, the same feature extraction procedure is

performed as in the training phase. The extracted features serve as the evidence to infer

the unknown state of the trained &$', which indicates whether a pixel belongs to a

vehicle or not. In this paper, we do not perform regionbased classification, which would

highly depend on results of color segmentation algorithms such as mean shift. There is no

need to generate multiscale sliding windows either. The distinguishing feature of the

proposed framework is that the detection task is based on pixel wise classification.

-owever, the features are extracted in a neighborhood region of each pixel. Therefore,


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the extracted features comprise not only pixellevel information but also relationship

among neighboring pixels in a region. +uch design is more effective and efficient than

regionbased or multi scale sliding window detection methods.

Advantage

Aore *ffective and efficient.

It does not re)uire a large amount of training samples

It increases the adaptability and the accuracy for detection in various aerial images


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,ardware re0uirements:

8rocessor ; #ny 8rocessor above == A-z.


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System Architecture


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(odules

12 !rame '%traction

32 Background color removal

42 !eature '%traction

52 $lassi)ication

62 /ost /rocessing

(odule Descri#tion

12 !rame '%traction

In module we read the input video and extract the number of frames from

that video.

ideo Frame *xtraction Frame Image


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32 Background color removal

In this module we construct the color histogram of each frame and remove

the colors that appear most fre)uently in the scene.These removed pixels do not

need to be considered in subse)uent detection processes. 8erforming background

color removal cannot only reduce false alarms but also speed up the detection

process.

Frame Image

"onstruct "olor

-istogram


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42 !eature '%traction

In this module we extract the feature from the image frame. In this module

we do the following *dge &etection, "orner &etection, color

Transformation and color classification.

52 $lassi)ication

In this module we perform pixel wise classification for vehicle detection using

&$'s. %&ynamic $ayesian 'etwork(. In the training stage, we obtain the conditional

probability tables of the &$' model via expectationmaximization algorithm by

providing the groundtruth labeling of each pixel and its corresponding observed features

from several training videos. In the detection phase, the $ayesian rule is used to obtain

the probability that a pixel belongs to a vehicle.

F

rame

Image

&

etect

the

*dge

&

etect

"

orner

Transfor

mc

olor

Frame

Image

Bet

pixel

values

#pply

&$'

&etect

vehicle


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62 /ost #rocessing

In this module we use morphological operations to enhance the detection

mask and perform connected component labeling to get the vehicle ob!ects. The

size and the aspect ratio constraints are applied again after morphological

operations in the post processing stage to eliminate ob!ects that are impossible to

be vehicles.

Data !low Diagram

&etected vehicle

#pply post

processing

Aask the detected

vehicles

&isplay the result


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UML Diagram

Use Case Diagram

ehicle

&etection

Input ideo&etected Frame

Frame*xtraction

Input ideo

&ete

cted

Fram

e

$ackgroun

d colorremoval

Feature

*xtraction

"lassificatio

n


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User

et In#ut Video

'%tract !rames

+emove

background color

'%tract !eature

$lassi)ication

/ost /rocessing

Detected Vehicle

Class Diagram


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(ain

J+tring fn

J,ideo video3

JImage frame

Jvoid readInput,ideo%(

Jvoid extractFrame%(

VehicleDetection

JImage frame

Jvoid remove$ackground%(

Jvoid extractFeature%(Jvoid classification%(

Jvoid post8rocessing%(

Jvoid store&etectedFrame%(

Activity Diagram


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Input ,ideo

*xtract Frame


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Training 8hase &etection 8hase

1 : Input Video()

2 : Extract Frame()

3 : Frames()

4 : Feature Extraction() 5 : Remove backround()

! : Features()

" : #$assi%ication()

& : 'ost 'reprocessin()

Collaboration diagram


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Training 8hase

&etection 8hase

1 : Input Video()

2 : Ext ract Frame()

3 : Frames()

4 : Feature Extraction()

5 : Remove backround()

! : Features()

" : #$assi%ication()

& : 'ost 'reprocessin()

Software Description

7ava -echnology

ava technology is both a programming language and a platform.

-he 7ava /rogramming Language

The ava programming language is a highlevel language that can be characterized

by all of the following buzzwords;

+imple

#rchitecture neutral

1b!ect oriented

8ortable

&istributed

-igh performance


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Interpreted

Aultithreaded


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any implementation of the ava A. That means that as long as a computer has a ava

A, the same program written in the ava programming language can run on Windows

7===, a +olaris workstation, or on an iAac.

-he 7ava /lat)orm

# platformis the hardware or software environment in which a program runs. We4ve

already mentioned some of the most popular platforms like Windows 7===, 6inux,

+olaris, and Aac1+. Aost platforms can be described as a combination of the operating

system and hardware. The ava platform differs from most other platforms in that it4s a

softwareonly platform that runs on top of other hardwarebased platforms.

The ava platform has two components;

TheJava Virtual Machine%ava A(

TheJava pplication !rogramming Interface%ava #8I(

Lou4ve already been introduced to the ava A. It4s the base for the ava platform and is

ported onto various hardwarebased platforms.

The ava #8I is a large collection of readymade software components that provide many

useful capabilities, such as graphical user interface %B?I( widgets. The ava #8I is

grouped into libraries of related classes and interfacesK these libraries are known as

pac"ages. The next section, What "an ava Technology &oM, highlights what

functionality some of the packages in the ava #8I provide.

The following figure depicts a program that4s running on the ava platform. #s the figure

shows, the ava #8I and the virtual machine insulate the program from the hardware.


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-,' 7AVA /LA-!9+(

'ative code is code that after you compile it, the compiled code runs on a specific

hardware platform. #s a platformindependent environment, the ava platform can be a

bit slower than native code. -owever, smart compilers, welltuned interpreters, and !ust

intime bytecode compilers can bring performance close to that of native code without

threatening portability.

#hat Can Java $echnology Do%

The most common types of programs written in the ava programming language are

appletsand applications. If you4ve surfed the Web, you4re probably already familiar with

applets. #n applet is a program that adheres to certain conventions that allow it to run

within a avaenabled browser.

-owever, the ava programming language is not !ust for writing cute, entertaining applets

for the Web. The generalpurpose, highlevel ava programming language is also a

powerful software platform. ?sing the generous #8I, you can write many types of

programs.

#n application is a standalone program that runs directly on the ava platform. # special

kind of application known as aserverserves and supports clients on a network. *xamples

of servers are Web servers, proxy servers, mail servers, and print servers. #nother

specialized program is aservlet. # servlet can almost be thought of as an applet that runs

on the server side. ava +ervlets are a popular choice for building interactive web

applications, replacing the use of "BI scripts. +ervlets are similar to applets in that they


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are runtime extensions of applications. Instead of working in browsers, though, servlets

run within ava Web servers, configuring or tailoring the server.

-ow does the #8I support all these kinds of programsM It does so with packages of

software components that provide a wide range of functionality. *very full

implementation of the ava platform gives you the following features;

The essentials; 1b!ects, strings, threads, numbers, input and output, data

structures, system properties, date and time, and so on.

#pplets; The set of conventions used by applets.

'etworking; ?


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!IU+' 5 ; 7AVA 3 SD

9DB$

Aicrosoft 1pen &atabase "onnectivity %1&$"( is a standard programming

interface for application developers and database systems providers. $efore 1&$"

became a de factostandard for Windows programs to interface with database systems,

programmers had to use proprietary languages for each database they wanted to connect

to. 'ow, 1&$" has made the choice of the database system almost irrelevant from a

coding perspective, which is as it should be. #pplication developers have much more

important things to worryabout than the syntax thatis needed to port their program from

one database to another when business needs suddenly change.

Through the 1&$" #dministrator in "ontrol 8anel, you can specify the particular

database that is associated with a data source that an 1&$" application program is

written to use. Think of an 1&$" data source as a door with a name on it. *ach door will

lead you to a particular database. For example, the data source named +ales Figures

might be a +@6 +erver database, whereas the #ccounts 8ayable data source could refer

to an #ccess database. The physical database referred to by a data source can reside

anywhere on the 6#'.


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Windows N does not install the 1&$" system files on your system.


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7DB$

In an effort to set an independent database standard #8I for ava, +un Aicrosystems

developed ava &atabase "onnectivity, or &$". &$" offers a generic +@6 database

access mechanism that provides a consistent interface to a variety of


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The goals that were set for &$" are important. They will give you some insight as to

why certain classes and functionalities behave the way they do. The eight design goals

for &$" are as follows;

1. Q! !evel "P#

The designers felt that their main goal was to define a +@6 interface for ava.

#lthough not the lowest database interface level possible, it is at a low enough level for

higherlevel tools and #8Is to be created. "onversely, it is at a high enough level for

application programmers to use it confidently. #ttaining this goal allows for future tool

vendors to /generate0 &$" code and to hide many of &$"4s complexities from the end

user.

$. Q! %onformance

+@6 syntax varies as you move from database vendor to database vendor. In an effort to

support a wide variety of vendors, &$" will allow any )uery statement to be passed

through it to the underlying database driver. This allows the connectivity module to

handle nonstandard functionality in a manner that is suitable for its users.

5. &D'% must (e implemental on top of common data(ase interfaces

The &$" +@6 #8I must /sit0 on top of other common +@6 level #8Is. This

goal allows &$" to use existing 1&$" level drivers by the use of a software

interface. This interface would translate &$" calls to1&$" and vice versa.

). Provide a &ava interface that is consistent *ith the rest of the &ava system

$ecause of ava4s acceptance in the user community thus far, the designers feel that they

should not stray from the current design of the core ava system.

+. ,eep it simple

This goal probably appears in all software design goal listings. &$" is no exception.

+un felt that the design of &$" should be very simple, allowing for only one method of


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completing a task per mechanism. #llowing duplicate functionality only serves to

confuse the users of the #8I.

-. se strong/ static typing *herever possi(le

+trong typing allows for more error checking to be done at compile timeK also, less

errors appear at runtime.

0. ,eep the common cases simple

$ecause more often than not, the usual +@6 calls used by the programmer are simple

+*6*"T4s, I'+*


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+ource "ode *ditor

+yntax highlighting for ava, ava+cript, HA6, -TA6, "++, +8, I&6

"ustomizable fonts, colors, and keyboard shortcuts

6ive parsing and error marking

8opup avadoc for )uick access to documentation

#dvanced code completion

#utomatic indentation, which is customizable

Word matching with the same initial prefixes

'avigation of current class and commonly used features

Aacros and abbreviations

Boto declaration and Boto class

Aatching brace highlighting

ump6ist allows you to return the cursor to previous modification

UI Builder

Fully WL+IWLB designer with Test Form feature

+upport for visual and nonvisual forms

*xtensible "omponent 8alette with preinstalled +wing and #WT components

"omponent Inspector showing a component2s tree and properties

#utomatic oneway code generation, fully customizable


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+upport for #WT>+wing layout managers, draganddrop layout customization

8owerful visual editor

+upport for null layout

Inplace editing of text labels of components, such as labels, buttons, and text

fields

ava$eans support, including installing, using, and customizing properties, events,

and customizers

isual ava$ean customization ability to create forms from any ava$ean

classes

"onnecting beans using "onnection wizard

Ooom view ability

Database Su##ort

&atabase schema browsing to see the tables, views, and stored procedures defined

in a database

&atabase schema editing using wizards

&ata view to see data stored in tables

+@6 and &&6 command execution to help you write and execute more

complicated +@6 or &&6 commands

Aigration of table definitions across databases from different vendors

Works with databases, such as Ay+@6, 8ostgre+@6, 1racle, I$A &$7,

Aicrosoft +@6 +erver, 8oint$ase, +ybase, Informix, "loudscape, &erby, and

more


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The 'et$eans I&* also provides fullfeatured refactoring tools, which allow you to

rename and move classes, fields, and methods, as well as change method parameters. In

addition, you get a debugger and an #ntbased pro!ect system.

Screen Shot


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Sam#le $oding

Import a!avax.swing.PK

public class Frame"apture implements "ontroller6istener

Q

8rocessor pK

1b!ect wait+ync R new 1b!ect%(K

boolean stateTransition1G R trueK

public boolean already8rnt R falseK

int tempR3K


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AainFrame mfK

Frame"apture%AainFrame m(

Q

mfRmK

S

public boolean open%Aedia6ocator ml(

Q

try

Q

p R Aanager.create8rocessor%ml(K

S

catch %*xception e(

Q

+ystem.err.println%9Failed to create a processor from the given url; 9 J e(K

return falseK

S

p.add"ontroller6istener%this(K

p.configure%(K

if %waitFor+tate%8rocessor."onfigured((

Q

+ystem.err.println%9Failed to configure the processor.9(K

return falseK

S

p.set"ontent&escriptor%null(K

Track"ontrol tcUV R p.getTrack"ontrols%(K

if %tc RR null(

Q


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+ystem.err.println%9Failed to obtain track controls from the processor.9(K

return falseK

S

Track"ontrol videoTrack R nullK

for %int i R =K i tc.lengthK iJJ(

Q

if %tcUiV.getFormat%( instanceof ideoFormat(

videoTrack R tcUiVK

else tcUiV.set*nabled%false(K

S

if %videoTrack RR null(

Q

+ystem.err.println%9The input media does not contain a video track.9(K

return falseK

S

+tring videoFormat R videoTrack.getFormat%(.to+tring%(K

&imension video+ize R parseideo+ize%videoFormat(K

try

Q

"odec codecUV R Q new 8ost#ccess"odec%video+ize(SK

videoTrack.set"odec"hain%codec(K

S

catch %*xception e(

Q

+ystem.err.println%9The process does not support effects.9(K

S


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p.prefetch%(K

if %waitFor+tate%8rocessor.8refetched((

Q

+ystem.err.println%9Failed to realise the processor.9(K

return falseK

S

p.start%(K

return trueK

S

public &imension parseideo+ize%+tring video+ize(

Q

int xR5==, yR7==K

+tringTokenizer strtok R new +tringTokenizer%video+ize, 9, 9(K

strtok.nextToken%(K

+tring size R strtok.nextToken%(K

+tringTokenizer size+trtok R new +tringTokenizer%size, 9x9(K

try

Q

x R Integer.parseInt%size+trtok.nextToken%((K

y R Integer.parseInt%size+trtok.nextToken%((K

S

catch %'umberFormat*xception e(

Q

+ystem.out.println%9unable to find video size, assuming default of 5==x7==9(K

S

return new &imension%x, y(K

S


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boolean waitFor+tate%int state(

Q

synchronized %wait+ync(

Q

try

Q

while %p.get+tate%( R state XX stateTransition1G(

wait+ync.wait%(K

S

catch %*xception e(

Q

S

S

return stateTransition1GK

S

public void controller?pdate%"ontroller*vent evt(

Q

if %evt instanceof "onfigure"omplete*vent YY evt instanceof


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synchronized %wait+ync(

Q

stateTransition1G R falseK

wait+ync.notify#ll%(K

S

S

else if %evt instanceof *nd1fAedia*vent(

Q

p.close%(K

+ystem.exit%=(K

S

S

static void pr?sage%(

Q

+ystem.err.println%9?sage; !ava Frame#ccess urlZ9(K

S

public class 8re#ccess"odec implements "odec

Q

void accessFrame%$uffer frame(

Q

long t R %long( %frame.getTime+tamp%( > 3=======f(K

if%frame.get6ength%(RR=(

Q

p.stop%(K

S

S


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protected Format supportedInsUV R new FormatUV Q new ideoFormat%null(SK

protected Format supported1utsUV R new FormatUV Q new ideoFormat%null(SK

Format input R null, output R nullK

public +tring get'ame%(

Q

return 98re#ccess "odec9K

S

public void open%( QS

public void close%( QS

public void reset%( QS

public FormatUV get+upportedInputFormats%(

Q

return supportedInsK

S

public FormatUV get+upported1utputFormats%Format in(

Q

if %in RR null(

return supported1utsK

else

Q

Format outsUV R new FormatU3VK

outsU=V R inK

return outsK

S

S

public Format setInputFormat%Format format(

Q


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input R formatK

return inputK

S

public Format set1utputFormat%Format format(

Q

output R formatK

return outputK

S

public int process%$uffer in, $uffer out(

Q

accessFrame%in(K

1b!ect data R in.get&ata%(K

in.set&ata%out.get&ata%((K

out.set&ata%data(K

out.setFlags%$uffer.F6#B['1[+L'"(K

out.setFormat%in.getFormat%((K

out.set6ength%in.get6ength%((K

out.set1ffset%in.get1ffset%((K

return $?FF*


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S

S

public class 8ost#ccess"odec extends 8re#ccess"odec

Q

public 8ost#ccess"odec%&imension size(

Q

supportedIns R new FormatUV Q new


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writer.write%outImage(K

ios.close%(K

S

catch %I1*xception e(

Q

+ystem.out.println%9*rror ;9 J e(K

S

S

long t R %long( %frame.getTime+tamp%( > 3=======f(K

if%frame.get6ength%(RR=(

Q

+ystem.out.println%9stop9(K

p.stop%(K

p.close%(K

1ption8ane.showAessage&ialog%new Frame%(,9Frame *xtracted9(K

mf.!$utton:.set*nabled%true(K

S

S

public +tring get'ame%(

Q

return 98ost#ccess "odec9K

S

private &imension sizeK

S

S


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$onclusion

In this paper, we have proposed an automatic vehicle detection system for aerial

surveillance that does not assume any prior information of camera heights, vehicle sizes,

and aspect ratios. In this system, we have not performed regionbased classification,

which would highly depend on computational intensive color segmentation algorithms

such as mean shift. We have not generated multiscale sliding windows that are not

suitable for detecting rotated vehicles either. Instead, we have proposed a pixelwise

classification method for the vehicle detection using &$'s. In spite of performing

pixelwise classification, relations among neighboring pixels in a region are preserved in

the feature extraction process. Therefore, the extracted features comprise not only pixel

level information but also regionlevel information. +ince the colors of the vehicles

would not dramatically change due to the influence of the camera angles and heights, we

use only a small number of positive and negative samples to train the +Afor vehicle

color classification. Aoreover, the number of frames re)uired to train the &$' is very

small. 1verall, the entire framework does not re)uire a large amount of training samples.We have also applied moment preserving to enhance the "anny edge detector, which

increases the adaptability and the accuracy for detection in various aerial images. The

experimental results demonstrate flexibility and good generalization abilities of the

proposed method on a challenging data set with aerial surveillance images taken at

different heights and under different camera angles. For future work, performing vehicle

tracking on the detected vehicles can further stabilize the detection results. #utomatic

vehicle detection and tracking could serve as the foundation for event analysis in

intelligent aerial surveillance systems.


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+e)erence

U3V


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UDV -. "heng and &. $utler, /+egmentation of aerial surveillance video using a mixture

of experts,0 in!roc& I''' Digit& Imaging Comput& .$ech& ppl&, 7==, p. CC.

UNV

Documents

Vehicle Detection Full Doc