FEATURE CLUSTERING FOR VEHICLE DETECTION AND TRACKING IN ROAD TRAFFIC SURVEILLANCE

被引：0

作者：

Yang, Jun ^{[1
]}

Wang, Yang ^{[1
]}

Ye, Getian ^{[1
]}

Sowmya, Arcot ^{[1
]}

Zhang, Bang ^{[1
]}

Xu, Jie ^{[1
]}

机构：

[1] Univ New S Wales, Natl ICT Australia, Sch Engn & Comp Sci, Sydney, NSW 2052, Australia

来源：

2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6 | 2009年

关键词：

Object detection; Tracking; MAP estimation; Monte Carlo methods; Clustering methods;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1145 / 1148

页数：4

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