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An Algorithm for Multiple Object Trajectory Tracking

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An Algorithm for Multiple Object Trajectory Tracking
Most tracking algorithms are based on the maximum a posteriori (MAP) solution of a probabilistic framework called Hidden Markov Model, where the distribution of the object state at current time instance is estimated based on current and previous observations. However, this approach is prone to errors caused by temporal distractions such as occlusion, background clutter and multi-object confusion. In this paper we propose a multiple object tracking algorithm that seeks the optimal state sequence which maximizes the joint state-observation probability. We name this algorithm trajectory tracking since it estimates the state sequence or "trajectory" instead of the current state. The algorithm is capable of tracking multiple objects whose number is unknown and varies during tracking. We introduce an observation model which is composed of the original image, the foreground mask given by background subtraction and the object detection map generated by an object detector. The image ...
Mei Han, Wei Xu, Hai Tao, Yihong Gong
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2004
Where CVPR
Authors Mei Han, Wei Xu, Hai Tao, Yihong Gong
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