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A Novel Segmentation A Novel Segmentation Method for Crowded Method for Crowded
ScenesScenes
Domenico Bloisi, Luca Iocchi,
Dorothy Monekosso, Paolo Remagnino
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
Page 2
Video surveillance tasks
A video surveillance system may accomplish a series of well-defined tasks:• To detect objects of interest
(we may want to detect all the moving cars in a street)[Yoneama et al. 2005, <long list>]
• To track objects of interest(we may want to know the exact number of people standing in a room) [Khan and Shah 2006, <long list>]
• To react to particular events(we may want to send an alarm if an unauthorized person enters a restricted area) [Leo et al. 2005, <long list>]
• …
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
Page 3
PB-KU
Visual modeling of people behaviors and interactions for professional training (PB-KU)
• The method is applied to the training of nurses in the School of Nursing in the Faculty of CISM at Kingston University [ http://www.healthcare.ac.uk ].
• The aim is to detect and track people in order to analyze their behavior
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
Page 4
Summary
• Project Overview
• Segmentation
• Height Image Algorithm
• Examples
• Results
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
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Features
• Background:– Dynamic background (indoor, crowded)
• Number of objects to track:– Up to 15 people in the scene
• Camera:– Two stereo cameras
• Evaluation method:– Evaluation on a on-site built data-set
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
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Hardware
Videre DesignSTH-MDCS
Intel Core 2 Duo2,0 GHz CPU
Mac mini
Videre DesignSTH-MDCS
Intel Core 2 Duo2,0 GHz CPU
Mac mini
Wireless connection
Firewireconnection
Firewireconnection
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
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Segmentation(Detecting objects of interest)
BackgroundEstimate
CurrentFrame
BackgroundModel
ForegroundExtraction
List ofDetected Objects
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
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Background Modeling
Background Imagecomputed from S
(the image displays only the higher
Gaussian values)
Set S of n images from a camera
Raw images
Artificial image
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
Page 10
Foreground Extraction(Background Subtraction Technique)
THRESHOLD T(based on illumination conditions)
blobs (Binary Large OBjectS)
>
T
current frame
foreground image
background image
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
Page 11
Background Subtraction Problems
Background subtraction is a fast and effective technique, but it presents a series of problems:
How to compute a correct background? [Heikkilä and Silven 1999] [Stauffer and Grimson 1999]
How to manage gradual and sudden illumination changes? [Bloisi et al. 2007]
How to manage high-frequencies background objects (such as artificial light flickering, windows) [Bloisi and Iocchi 2008]
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
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Proposed Solution
Background Subtraction +Stereo Vision +
Edge Detection +Height Image Algorithm
Advantages:robust and efficient foreground extraction, shadow suppression, 3D information, non-moving object filtering, accurate multiple object segmentation.
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
Page 13
System architecture PB-KU
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
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Height ImageAlgorithm
t: minimum area for a blob to be considered of interest
A: set of found activity blobs
F: final set of the segmented objects we are searching for
H: the set of height images.
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
Page 15
Height Image
a) Active blobsb) Height imagec) Segmented
image
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
Page 16
Example
Segmented image Ground plane view
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
Page 17
Example
Crowd flowAnalysis
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
Page 18
Evaluation and Metrics
• Evaluation On-site dataset
• Metrics Scene accuracy
A is the average accuracy
n
nnai
ˆ
1
Number of detected people
Number of people in the scene
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
Page 19
Segmentation Results
Segmentation Accuracyon 100 randomly chosen images
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
Page 21
Conclusions
• Summary of results Accurate segmentation even in
case of 15 people in the scene Real-time computation Ground plane view projection for
crowd flow analysis
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
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Future Work (1)
Add Radio Frequency Identifiers (RFID) to stereo for helping segmentation and dealing with occlusions
RFID
Identity
LocalizationStereo
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
Page 23
Future Work (2)
Crowd flow analysis based on ground plane projection
• ExampleHow many people are near a bed in event of an emergency?
04/18/23A Novel Segmentation Method for Crowded Scenes – VISAPP 2009
Page 24
References
- A. Yoneama C.H. Yeh, C.C.J. Kuo. Robust Vehicle and Traffic Information Extraction for Highway Surveillance, JASP(2005), No. 14, pp. 2305-2321, 2005. - M. Leo, T. D’Orazio, A. Caroppo, T. Martiriggiano P. Spagnolo. Automatic Monitoring of Forbidden Areas to Prevent Illegal Accesses ICAPR (2), pp. 635-643, 2005. - S. Khan and M. Shah. A multiview approach to tracking people in crowded scenes using a planar homography constraint. In ECCV (4), pp. 133–146, 2006. - Y.T. Tsai, H.C. Shih and C.-L. Huang. Multiple human objects tracking in crowded scenes. In ICPR ’06, pp. 51–54, 2006.- J. Heikkilä, O. Silven. A real-time system for monitoring of cyclists and pedestrians. Proc. 2° IEEE International Workshop on Visual Surveillance, pp. 74-81, 1999.-C. Stauffer, W. Grimson. Adaptive background mixture models for real-time tracking. (CVPR'99), pp.246-252, 1999.- D. Bloisi, L. Iocchi, G.R. Leone, R. Pigliacampo, L. Tombolini, L. Novelli. A Distributed Vision System for Boat Traffic Monitoring in the Venice Grand Canal (VISAPP), pp. 549-556, 2007.- D. Bloisi and L. Iocchi. ARGOS - A Video Surveillance System for Boat Traffic Monitoring in Venice. IJPRAI, 2008.
A Novel Segmentation A Novel Segmentation Method for Crowded Method for Crowded
ScenesScenes
Domenico Bloisi, Luca Iocchi,
Dorothy Monekosso, Paolo Remagnino