Feature clustering for vehicle detection and tracking in road traffic surveillance

Authors:
Jun Yang;Yang Wang;Getian Ye;Arcot Sowmya;Bang Zhang;Jie Xu
Affiliations:
National ICT Australia and School of Computer Science and Engineering, The University of New South Wales;National ICT Australia and School of Computer Science and Engineering, The University of New South Wales;National ICT Australia and School of Computer Science and Engineering, The University of New South Wales;National ICT Australia and School of Computer Science and Engineering, The University of New South Wales;National ICT Australia and School of Computer Science and Engineering, The University of New South Wales;National ICT Australia and School of Computer Science and Engineering, The University of New South Wales
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 6
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Abstract

In this paper, we formulate the feature clustering problem for vehicle detection and tracking as a general MAP problem and solve it using MCMC. The proposed approach exhibits two advantages over existing methods: general Bayesian model can handle arbitrary objective functions and MCMC guarantees global optimal solution. Our algorithm is validated on real-world traffic video sequences, and is shown to outperform the state-of-the-art approach.