Multiple Objects Tracking

Visual Object Tracking

Xu YanQuantitative Imaging Laboratory1Xu Yan

Advisor: Shishir K. ShahQuantitative Imaging LaboratoryComputer Science DepartmentUniversity of HoustonMultiple Object Tracking - ObjectiveXu YanQuantitative Imaging Laboratory2To develop Human tracking system by single camera in outdoor environment

Multiple Object Tracking - ChallengesThe core challenges of the visual object tracking task is the enormous unpredictable variations in targets due to :

3Xu YanQuantitative Imaging Laboratoryenvironment changestarget deformationspartial occlusionsabrupt motioncamouflagelow image qualities

Multiple Object Tracking - Framework4Human DetectorPredictorPrior KnowledgeInitializeData AssociationHuman TrajectoriesHuman DetectionTrackerXu YanQuantitative Imaging LaboratoryHuman DetectionNow we give the tracker manual initialization in the first frame.

5Xu YanQuantitative Imaging Laboratory

Prediction - Social Interaction6

Xu YanQuantitative Imaging LaboratoryData Association7

Blob regionPrediction regionComparisonFrame tFrame t+1Likelihood of every particle

Xu YanQuantitative Imaging LaboratoryMultiple Object Tracking Results8Xu YanQuantitative Imaging LaboratoryOUR trackerBPF trackerMCMC trackerVTD tracker

Contribution and future workConclusionThe 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 modelMore robust data association modelPaperXu 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 102.1-102.11. BMVA Press, September 2011.

9Xu YanQuantitative Imaging Laboratory