chairman: dr. hung-chi yang presenter: fong- ren sie advisor: dr. yen-ting chen date: 2013.10.16

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Fast and Robust Algorithm of Tracking Multiple Moving Objects for Intelligent Video Surveillance Systems

Chairman: Dr. Hung-Chi YangPresenter: Fong-Ren SieAdvisor: Dr. Yen-Ting ChenDate: 2013.10.16

Jong Sun Kim, Dong Hae Yeom, and Young Hoon Joo,

IEEE Transactions on Consumer Electronics, Vol. 57, No. 3,

August, 2011

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OutlineIntroductionMethodologyResultsConclusionsReferences

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IntroductionThe traditional video surveillance

system◦Closed-circuit televisions (CCTV)◦Digital video recorders (DVR)

Disadvantages◦Need someone to monitor and

searchReal time intelligent video

surveillance systems◦High-cost and low-efficiency

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IntroductionThe intelligent video surveillance

system is a convergence technology◦Detecting and tracking objects◦Analyzing their movements◦Responding

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IntroductionTracking 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

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IntroductionEliminate 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).

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IntroductionRGB BM with a new sensitivity

parameter to extract moving regions

Morphology schemes to eliminate noises and labeling to group the moving objects.

MethodologyDETECTING 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.

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MethodologyGray-scale BM

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MethodologyRGB color model

Prevent excessive attenuationShorter execution time

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MethodologyBinary image

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MethodologyThe 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

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MethodologyTracking 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.

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Methodology

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Results

(d) The 169th frame

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Results

The error of the predicted position of each group

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Results

The processing time of the proposed method

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ConclusionsDetecting 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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[4] R. M. Haralick, S. R. Stemberg, and X. Zhuang, “Image Analysis Using Mathematical Morphology,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-9, No. 4, pp. 532-550. 1987.

[5] I. Haritaoglu, D. Harwood, and L. S. Davis, “W4: Real-time Surveillance of People and Their Activities,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No.8, pp. 809-830, Aug. 2000.

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References [6] M. Haseyama and Y. Kaga “Two-phased Region Integration

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[7] O. Javed and M. Shah, “Tracking and Object Classification for Automated Surveillance,” 7th European Conference on Computer Vision, Lecture Notes in Computer Science 2353, pp. 343–357, 2002.

[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 Transactions on Consumer Electronics, Vol. 55, No. 2, pp. 591-598,May 2009.

[10] D. Makris and T. Ellis, “Automatic Learning of an Activity-based Semantic Scene Model,” Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 183-188, Jul. 2003.

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References [11] M. H. Sedky, M. Moniri, and C. C. Chibelushi, “Classification of

Smart Video Surveillance Systems for Commercial Applications,” IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 638-643, Sep. 2005.

[12] C. Stauffer and W. Grimson, “Learning Patterns of Activity Using Real Time Tracking,” IEEE Transactions on Pattern Analysis and machine Intelligence, Vol. 22, No.8, pp. 747-767, Aug. 2000.

[13] M. Valera and S. A. Velastine, “A Review of the State-of-art in 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 Object-Tracking 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 Distortion,” IEEE Transactions on Consumer Electronics, Vol. 53, No. 3, pp. 1177-1185,Aug. 2007.

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Thank you for your attention