Egocentric View Transition for Video Monitoring in a Distributed
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Transcript Egocentric View Transition for Video Monitoring in a Distributed
Fast and Robust Algorithm of Tracking
Multiple Moving Objects for Intelligent
Video Surveillance Systems
Jong Sun Kim, Dong Hae Yeom, and Young Hoon Joo,
IEEE Transactions on Consumer Electronics,Vol. 57, No. 3,
August, 2011
Chairman: Dr. Hung-Chi Yang
Presenter: Fong-Ren Sie
Advisor: Dr. Yen-Ting Chen
Date: 2013.10.16
1
Outline
Introduction
Methodology
Results
Conclusions
References
2
Introduction
The traditional video surveillance system
◦ Closed-circuit televisions (CCTV)
◦ Digital video recorders (DVR)
Disadvantages
◦ Need someone to monitor and search
Real time intelligent video surveillance
systems
◦ High-cost and low-efficiency
3
Introduction
The intelligent video surveillance system is
a convergence technology
◦ Detecting and tracking objects
◦ Analyzing their movements
◦ Responding
4
Introduction
Tracking Multiple Moving Objects for
Intelligent Video Surveillance Systems
◦ The basic technologies of the intelligent video
surveillance systems.
◦ To detect and track the specific moving
objects.
◦ Eliminate the environmental disturbances
5
Introduction
Eliminate the environmental disturbances
◦ The Bayesian method such as the Particle
Filter(PF) or the Extended Kalman Filter (EKF)
◦ Background modeling (BM) or the Gaussian
mixture model (GMM).
6
Introduction
RGB BM with a new sensitivity parameter
to extract moving regions
Morphology schemes to eliminate noises
and labeling to group the moving objects.
7
Methodology
DETECTING MOVING OBJECTS
◦ Extraction of Moving Objects
BM involves the loss of image information
compared with the color BM using RGB and HSI
color space models
◦ Gray-scale BM
Image information is excessively attenuated.
◦ RGB color model
Very sensitive to even small changes caused by light
scattering or reflection.
Methodology
Gray-scale BM
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Methodology
RGB color model
Prevent excessive attenuation
Shorter execution time
10
Methodology
Binary image
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Methodology
The group tracking
◦ Prevent the problems of the individual
tracking
◦ A grouping scheme is required to classify
moving objects into several groups
◦ The 4-directional blob labeling is employed to
group moving objects
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Methodology
4-directional blob-labeling
13
Methodology
Tracking moving object
◦ Predicting the position of each group
◦ Recognizing the homogeneity of each group in
the sequential frames
◦ identifying the newly appearing and
disappearing groups.
14
Methodology
15
Results
(d) The 169th frame
16
Results
The error of the predicted position of each group
17
Results
The processing time of the proposed method
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Conclusions
Detecting and tracking multiple moving
objects
◦ Can be applied to consumer electronics
◦ The robustness and the speed
◦ The robustness against the environmental
influences
◦ The high-speed of the image processing
◦ The method is intended for a fixed camera
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References
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[4] R. M. Haralick, S. R. Stemberg, and X. Zhuang, “Image Analysis
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References
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Approach for Effective Pedestrian Detection in Low Contrast
Images” IEEE International Conference on Consumer Electronics, pp. 1-2,
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[8] S. Kang, J. Paik, A. Koschan, B. Abidi, and A. Abidi, “Real-time Video
Tracking Using PTZ Cameras,” Proceedings of SPIE 6th International
Conference on Quality Control by Artificial Vision, Vol. 5132, pp. 103-111,
2003.
[9] W. Lao, J. Han, and H. N. Peter, “Automatic Video-based Human
Motion Analyzer for Consumer Surveillance System” IEEE
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2009.
[10] D. Makris and T. Ellis, “Automatic Learning of an Activity-based
Semantic Scene Model,” Proceedings of IEEE Conference on Advanced
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References
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Smart Video Surveillance Systems for Commercial Applications,”
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638-643, Sep. 2005.
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Distributed Surveillance Systems,” IEE Intelligent Distributed Video
Surveillance Systems, pp.1-30, 2006.
[14] Y. Zhai, M. B.Yeary, S. Cheng, and N. Keharnavaz, “An ObjectTracking Algorithm Based on Multiple-model Particle Filtering with
State Partitioning,” IEEE Transactions on instrumentation and
measurement,Vol.58, No.5, pp. 1797-1809, May 2009.
[15] R. Zhang, S. Zhang, and S.Yu, “Moving Objects Detection
Method Based on Brightness Distortion and Chromaticity
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Thank you for your attention
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