Real Time Multiple Object Tracking and OcclusionReasoning Using Adaptive Kalman Filters

سال انتشار: 1390
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 1,420

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شناسه ملی سند علمی:

ICMVIP07_100

تاریخ نمایه سازی: 28 مرداد 1391

چکیده مقاله:

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.

نویسندگان

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