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Multiple Object Detection

By- Saurabh Kumar Reg. No.-15MCA1009

Guided By: Dr.Priyadarshini.J

05/02/2023RBL Fall 2015 Review 1I

. Abstract

. Introduction

. Existing System

. Proposed work

. Module

. Implementation details

. Screen shots

. Software/Tools required

. References

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Contents

In this paper; the proposed methods detect various multiple object detection using image processing. Object detection is a main role in image processing. This paper proposed the use of two methods Haar Training and Ada Boost Classifier Algorithm for training for multiple detection.

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Abstract

Object detection and tracking - identification of an object in image and locate .

Multiple object detection - very difficult topic in computer vision.Single object trackingMulti object tracking

Tracking detected objects frame by frame in video is a significant and difficult task.

Haar Training is main role in image processing for object detection and object Tracking.

Object Tracking uses :- motion-based recognition, video index, etc

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Introduction

Now a days we are facing various drawback.

Object are not perfect aligned in Training.

Multiple Kernel Template . Identify the color of images. Each image using multiple segmentation.

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Existing System

The supervised learning - Haar Training the system with the target for automatic detection.

The Haar features used because they effectively capture different image details, and it is fast algorithm .

The Haar Training object detector used to detect the location of a car in a image.

First detect the object, then track object by the use the vision.

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Proposed work

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Module

Fig. 1. Process of Object Detection

Object Training Object

DetectionObject

Tracking

1.Multiple Object Training:- Haar Training is used for multiple object detection in the

image. Training the provide the detecting facilities.

2.Multiple Object Detection:- The main work multiple image detecting together.

3.Multiple Object Tracking:- In this tracking section detecting the image one by one .

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Cont..

Object Tracking 4

Object Detection

2

Video sequence

1Object

Classification 3

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Implementation details

Fig. 2 Detection Details

.

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Detailed Architecture

Video sequence

Object Detection

Object Classification

Object Tracking

Frame DifferencingBackground Subtraction

Shape-based

Color-based

Texture-based

Point-basedKernel-based

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Screen shots

Fig. 3 Video Format Details

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Cont..

Fig. 4 Snapshot picture

MATLAB R2013a Windows 8.1

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Software/Tools required

[1] Chensheng Sun and Kin-Man Lam,”Multiple-Kernel,Multiple-Instance Similarity Features for Efficient Visual Object Detection” IEEE Transactions on “ vol. no (22), issue number (8) pp. 3050-3061, 2013.

[2] Asako Kanezaki1, Sho Inaba 1, Yoshitaka Ushiku1, Yuya Yamashita1, Hiroshi Muraoka1, Yasuo Kuniyoshi1 and Tatsuya Harada1,” Hard Negative Classes for Multiple Object Detection” “IEEE Transaction on “pp.3066-3073,2014.

[3] Hongliang Li, Fanman Meng, and King Ngi Ngan,” Co-Salient Object Detection From Multiple Images ”, “IEEE Transaction on” vol. no (15), issue number(8) pp. 1896- 1909,2013.

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References

[4]. Srishtee Jain and Surendra Chadokar, “A Object in Image processing” International

Journal of Electrical, Electronics andComputer Engineering 4(2): 26-29(2015).

[5] Xin Mao, Feihu Qi, Wenjia Zhu, “Multiple –part based Pedestrian Detection Using Interfering Object Detection” IEEE 0-7695-2875-9/07 2007.

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Conti..

THANK YOU

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