1. Fast and Robust Algorithm of Tracking Multiple Moving Objects for Intelligent Video Surveillance Systems Jong Sun Kim, Dong Hae Yeom, and Young Hoon Joo,2011

2. Goal detecting and tracking multiple moving objects real-time detecting robustness against the environmental influences and the speed

3. Outline Introduction Previous Methods Detecting Moving Objects – Extraction of Moving Objects – Grouping Moving Objects Tracing Moving Objects Implementation and Experiment Conclusions

4. Introduction In the traditional systems that a person should always monitor video. intelligent video surveillance systems are high-cost and low-efficiency Environment affects a lot. This paper propose a method detecting and tracking multiple moving objects in real-time.

5. Previous Methods particle filter,extended Kalman filter Background modeling (BM) or the Gaussian mixture model (GMM) gray-scale BM shows the image information is excessively attenuated.

6. Extraction of Moving Objects Using RGB color BM instead of gray-scale BM Each pixels will compare with previous pixels in little group. If it is stationary, the pixels will be black. The parameter δ is proposed to overcome the sensitivity problem. δ would be different on different camera.

7. Extraction of Moving Objects

10. Grouping Moving Objects The individual tracking of neighboring or overlapping objects requires a lot of computational capacity. The 4-directional blob-labeling is employed to group moving objects.

11. Grouping Moving Objects Contour Tracing

12. Grouping Moving Objects its initial search position is set to be d+2 (mod 8)

13. Tracing Moving Objects

15. Implementation and Experiment The 33Mbit IP camera provides the input image with 704x480 pixels. The surveillance image is transmitted through Internet. 2.66GHz CPU and 4GB RAM PC for the image signal processing and the proposed algorithm.

16. Implementation and Experiment

19. Conclusions Real-time detecting and tracing Only for fixed camera. Future works can be on predicted position.