Progress Report Meng-Ting Zhong 2015/9/10

Real-Time Multi-Target Tracking

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

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

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

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

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

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

Dataset(1/2)

Dataset(2/2)

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

Traditional Evaluation Method(2/2) For tracking

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

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