Visual Object Tracking Xu Yan Quantitative Imaging Laboratory 1 Xu Yan Advisor: Shishir K. Shah Quantitative Imaging Laboratory Computer Science Department University of Houston

Multiple Object Tracking - Objective Xu Yan Quantitative Imaging Laboratory 2 To develop Human tracking system by single camera in outdoor environment

Multiple Object Tracking - Challenges The core challenges of the visual object tracking task is the enormous unpredictable variations in targets due to : 3 Xu Yan Quantitative Imaging Laboratory  environment changes  target deformations  partial occlusions  abrupt motion  camouflage  low image qualities

Multiple Object Tracking - Framework 4 Human Detector Predictor Prior Knowledge Initialize Data Association Human Trajectories Human Detection Tracker Xu Yan Quantitative Imaging Laboratory

Human Detection Now we give the tracker manual initialization in the first frame. 5 Xu Yan Quantitative Imaging Laboratory

Prediction - Social Interaction 6 Xu Yan Quantitative Imaging Laboratory

Data Association 7 Blob region Prediction region Comparison Frame t Frame t+1 Likelihood of every particle Xu Yan Quantitative Imaging Laboratory

Multiple Object Tracking – Results 8 Xu Yan Quantitative Imaging Laboratory OUR trackerBPF tracker MCMC trackerVTD tracker

Contribution and future work Conclusion – The experimental results demonstrate that the proposed method enables tracking of pedestrians in complex scenes with occlusions and varying interaction behaviors. Future work – Incorporate online updating observation model – More robust data association model Paper – Xu Yan, Ioannis Kakadiaris and Shishir Shah. Predicting Social Interactions for Visual Tracking. In Jesse Hoey, Stephen McKenna and Emanuele Trucco, Proceedings of the British Machine Vision Conference, pages BMVA Press, September Xu Yan Quantitative Imaging Laboratory