1. Multiple People Detection and Tracking with Occlusion Presenter: Feifei Huo Supervisor: Dr. Emile A. Hendriks Dr. A. H. J. Stijn Oomes Information and Communication Theory (ICT) Group Delft University of Technology Nov. 29th, 2007

2. Outline  Definition and objective of the project  2-D Human Model  Particle Filter For People Detection and Tracking  Color histogram matching  Experiment Results  Next

3. Definition and Objective of the Project Objective :  Develop fast and robust algorithms that can detect, track, and model accurately and robustly individual persons in the real 3D world Challenge :  Indoor scene with lighting condition changing  Multi-person tracking with occlusion

4. Overview of Proposed Algorithm  Foreground binary image extraction  People model definition  People detection and tracking using particle filter  Multi-people tracking with occlusion

5. Foreground Binary Image Current Frame Background gray image Gray image RGB to GRAY Background image RGB to GRAYThreshold

6. 2-D Human Model The geometric properties of silhouette are used to determine if the moving objects has a human shape. It is convenient to describe the 2-D model mathematically, where a human hypothesis is a vector of parameters whose values are positions and size.

7. 2-D Human Model  How to use this shape feature? (I)(II) (III) (I) and (II)----Low Score, (III)---High Score. Conclusion: Only when the position and scale of this shape model fit people well, we can get high fitness value.

8. Particle Filter For Detection and Tracking  A particle set is generated with an initial distribution.  Then the observation steps take place and the weights are calculated from the observation sample.  The new set of weights form the estimation to the posterior (and therefore the prior for the next iteration).

9. Initialization of the detection and tracking system Step1. Get foreground binary image Step2. Foreground blob segmentation Step3. Size filter to get candidate blob with people Step4. Initialize particle filter with Gaussian distribution Step1Step2Step3Step4

10. Particle filter for people detection and tracking Iteration=20 Initial Frame Particle systemDetection result

11. Multiple people tracking with head model After the convergence of the head-should-upper body model, we can set the nominal scale of the head model for tracking people. Head model can provide more accurate position and scale information of the people. Iteration

12. Multiple people tracking with occlusion Demo2 Demo1

13. Use discriminative feature to identify different people Objective: 1. Find out whether person A occludes person B, or the other way around. 2. A group of people detection and tracking. Solution: 1. Use color information to distinguish different people. 2. The parameters of 2-D human model are increased into positions, size and color. P=(x, y, scale, color)

14. Color histogram similarity 1. Initialize color histogram before occlusion. 2. Calculate color histogram similarity when occlusion. 3. Identify individual people after occlusion. Demo3

15. To be continued 1. Evaluation of the algorithm 2. Testing with different videos Objective: 1. Optimize the parameters in the algorithm. 2. Increase the implementation speed.

16. Thanks for your attention ! ?