1. Progress Report
Meng-Ting Zhong
2015/9/10

2. Real-Time Multi-Target Tracking

3. System Overview
Re-Identification
Object Detection: Discriminatively Trained Part Based Models
Intra-Camera Tracking: Particle Filter

4. Requirements Online Not a matching problem after video collection
Distributed To minimize data transmission bandwidth
Easy to train Need a simple method to train in a short time
Computational efficient Avoid complicated algorithm

5. Traditional Person Re-ID(1/4)
With Deep Learning

6. Traditional Person Re-ID(2/4)
Maintain Consistency

7. Traditional Person Re-ID(3/4)
With Video Ranking

8. Traditional Person Re-ID(4/4)
Over Multiple Kinect Cameras

9. Dataset(1/2)

10. Dataset(2/2)

11. Traditional Evaluation Method(1/2)
For re-identification

12. Traditional Evaluation Method(2/2)
For tracking

13. Proposed Evaluation Method
Human detection: Recall and precision
Tracking: Mostly tracked(MT), partially tracked(PT), mostly lost(ML), fragmentation, ID switch
Re-ID: Crossing Fragment(X-Frag) Rate, Crossing ID switch(X-ID) Rate

14. Comparison of Algorithms and Training Sample Sizes
Logistic Regression
Radial Basis Function Network
Maximum-
Likelihood
Classification