Real Time Multiple Object Tracking and OcclusionReasoning Using Adaptive Kalman Filters
Publish Year: 1390
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICMVIP07_100
تاریخ نمایه سازی: 28 مرداد 1391
Abstract:
Object tracking in image sequences is one of thefundamental steps in designing intelligent surveillance systems.The fact that Multiple Object Tracking (MOT) algorithmsrequires occlusion reasoning and data association, makes designof these algorithms much more complicated than Single ObjectTracking (SOT) algorithms. A new method for real time MOT isintroduced in this paper to efficiently solve the occlusion issue.Background subtraction has been employed for detecting objectsin this method. In order to computing data association betweenobject in current frame with previous tracks, a new distancefunction is introduced for implementing General NearestNeighbor (GNN) method. In the case in which objects are in adistance, Kalman filter with constant measurement noisecovariance has been used for tracking objects however whenocclusion happens, measurement noise covariance will beadapted by result of a local template matching in whichcorrelation coefficients method has been employed. Experimentalresults confirm the efficiency and robustness of proposed methodfor MOT and occlusion reasoning.
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Authors
Mohammad Azari
AIMS Research Lab., Electrical Engineering Dept.Amirkabir University of TechnologyTehran, IRAN
Ahmad Seyfi
AIMS Research Lab., Electrical Engineering Dept.Amirkabir University of TechnologyTehran, IRAN
Amir Hossein Rezaie
AIMS Research Lab., Electrical Engineering Dept.Amirkabir University of TechnologyTehran, IRAN