Chairman: Dr. Hung-Chi Yang Presenter: Fong- Ren Sie Advisor: Dr. Yen-Ting Chen Date: 2013.10.16

1. Fast and Robust Algorithm of Tracking Multiple Moving Objects for Intelligent Video Surveillance Systems Chairman: Dr. Hung-Chi Yang Presenter: Fong-RenSie Advisor: Dr. Yen-Ting Chen Date: 2013.10.16 Jong Sun Kim, Dong HaeYeom, and Young HoonJoo, IEEE Transactions on Consumer Electronics, Vol. 57, No. 3, August, 2011

2. Outline • Introduction • Methodology • Results • Conclusions • References

3. 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

4. Introduction • The intelligent video surveillance system is a convergence technology • Detecting and tracking objects • Analyzing their movements • Responding

5. Introduction • Tracking Multiple Moving ObjectsforIntelligent Video Surveillance Systems • The basic technologies of the intelligent video surveillance systems. • To detect and track the specific movingobjects. • Eliminate the environmental disturbances

6. 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).

7. Introduction • RGB BM with a new sensitivity parameterto extract moving regions • Morphology schemes to eliminate noisesand labeling to group the moving objects.

8. 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.

9. Methodology • Gray-scale BM

10. Methodology • RGB color model • Prevent excessive attenuation • Shorter execution time

11. Methodology • Binary image

12. Methodology • The group tracking • Prevent the problems of the individual tracking • A grouping scheme is required to classifymoving objects into several groups • The 4-directional blob labeling is employed to group moving objects

13. Methodology • 4-directional blob-labeling

14. Methodology • Tracking moving object • Predicting the position of each group • Recognizing the homogeneity of each group in the sequential frames • identifying the newly appearing anddisappearing groups.

15. Methodology

16. Results • (d) The 169th frame

17. Results • The error of the predicted position of each group

18. Results • The processing time of the proposed method

19. 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

20. References • [1] C. Chang, R. Ansari, and A. Khokhar, “Multiple Object Tracking with Kernel Particle Filter,” Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.1, pp.566-573, May 2005. • [2] F. Chang, C. J. Chen, and C. J. Lu. “A Linear-time Component Labeling Algorithm Using Contour Tracing Technique,” Computer Vision and Image Understanding, Vol. 93, No. 2, pp. 206-220, 2004. • [3] A. Hampapur, L. Brown, J. Connell, A. Ekin, N. Haas, M. Lu, H. Merkl, S. Pankanti, A. Senior, C. Shu, and Y. L. Tian, “Smart Video Surveillance,” IEEE Signal Processing Magazine, Vol. 22, No.2, pp. 38-51, Mar. 2005. • [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.

21. References • [6] M. Haseyama and Y. Kaga “Two-phased Region Integration Approach for Effective Pedestrian Detection in Low Contrast Images” IEEE International Conference on Consumer Electronics, pp. 1-2, Jan. 2008. • [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.

22. 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.

23. Thank you for your attention