Multi-object trajectory tracking

8 years 11 months ago
Multi-object trajectory tracking
The majority of existing tracking algorithms are based on the maximum a posteriori (MAP) solution of a probabilistic framework using a Hidden Markov Model, where the distribution of the object state at the current time instance is estimated based on current and previous observations. However, this approach is prone to errors caused by distractions such as occlusions, background clutters and multi-object confusions. In this paper we propose a multiple object tracking algorithm that seeks the optimal state sequence that maximizes the joint multi-object state-observation probability. We call this algorithm trajectory tracking since it estimates the state sequence or “trajectory” instead of the current state. The algorithm is capable of tracking unknown time-varying number of multiple objects. We also introduce a novel 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 detec...
Mei Han, Wei Xu, Hai Tao, Yihong Gong
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where MVA
Authors Mei Han, Wei Xu, Hai Tao, Yihong Gong
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