1. Abandoned Object Detection for Public Surveillance Video Student: Wei-Hao Tung Advisor: Jia-Shung Wang Dept. of Computer Science National Tsing Hua University

2. Outline Introduction System Environment Proposed Method Experiment Results Conclusion

3. Motivation architecture Index & Search Digital Online Surveillance 1 st video surveillance Close Circuit Television System 2 nd video surveillance Digital Video Recorder ٧٧ 3 rd video surveillance IP surveillance ٧٧٧

4. Applications of Video Surveillance System Security  Surveillance of housing, public area  Detecting or tracking suspicious objects [5][6][7][8] Behavior analysis  Segmentation of the human body [2]  Classify the behavior of the human [1][2]

5. Related Work Referenced background image Detecting busy environment Update pixel info. when busy environment Gibbins, and Newsam, etc.[10] ٧٧ Sacchi & Regazzoni [9] ٧٧ Yang, and Pan, etc.[5] ٧٧ Our proposed system ٧٧٧

6. Mixture of Gaussian (MoG) Used to model background 1 frame # weight 0 x Background distribution

7. Problems of MoG When environment is with such greatly many moving objects that the previous suppose is wrong 1 frame # weight 0 Background distribution x

8. System environment Indoor public places with greatly many people Characteristics  Fewer illumination variance  Pedestrians are full of the scene Example: Taipei Metro

9. System environment 1. No Pedestrians 2. Some Pedestrians 3. Many Pedestrians

10. System Environment No Pedestrains Some Pedestrains Many Pedestrains More People background

11. Problem Definition Goal  Only add pixel intensity history of stationary objects to MoG, that is, filter ones of moving objects Problem  How to separate Stationary Objects from Moving Objects ? Motion vector

12. Proposed Method

13. 1. Model background 2. Set 1st distribution Mean as background intensity in every pixel 3. Read Next Frame 4. Process Frame With Our Method

14. Other Problems The object edge is often with larger illumination variance  Solution: Extend lost object check Some background area is with larger illumination variance  Solution: Reference to chrominance information (UV in YUV)

15. Proposed Method frame Update MoG Yes No Update MoG Is last pixel of line ? Yes No x YesNo Is 1st FG pixel like the previous frame ? Is k-th continuous FG pixel ? If1-th FG pixel, record pixel info. Yes Next pixel if in background distribution? (refer to chrominance info.) No

16. Experiments Environment: Metro in Taipei Period: 100secs (3000 frames) Region of Scene: 49x296 Monochromatic

17. Results originimproved False negative False positive improved 9.4%8.8% show the one vertical line result

18. Conclusions We design a novel system to find the stationary object in busy public place Our system will only update stationary pixel to MoG and is with highly correct true positive ratio

19. Reference 1. Image stabilization algorithms for video-surveillance applications 2. Segmentation and tracking of interacting human body parts under occlusion and shadowing 3. Effective Gaussian mixture learning for video background subtraction 4. Real-time change detection for surveillance in public transportation 5. Multiple layer based background maintenance in complex environment 6. Multiple moving objects tracking for video surveillance systems 7. Joint video-shot and layer indexing in video-surveillance application 8. Fusion of two different motion cues for intelligent video surveillance 9. A distributed surveillance system for detection of abandoned objects in unmanned railway environments 10. Detecting suspicious background changes in video surveillance of busy scenes

20. Proposed Method our method Process Next Vertical Line of Frame From Top Pixel frame Is the last vertical line ? Yes No

21. Proposed Method frame Update MoG Yes No Update MoG Is last pixel of line ? Yes No x Yes No Is this pixel like the previous frame ? Record this pixel as 1st not BG point Next pixel if in background distribution?

22. Related Work Video Surveillances focus on  Classify behavior of foreground objects [1] [2][5]  Model non-stationary objects with improving convergence speed and stability [3]  Suppress false positive like background illumination variations or ghost [4] [6]  Detecting abandoned package [5][7][8][9] Most video surveillance systems model pixel value history with Gaussian distributions (MoG)

23. Proposed Method