Sequential Monte Carlo tracking by fusing multiple cues in video sequences

被引：113
作者：
Brasnett, Paul [1 ]
Mihaylova, Lyudmila
Bull, David
Canagarajah, Nishan
机构：
[1] Univ Lancaster, Dept Commun Syst, Lancaster LA1 4WA, England
[2] Univ Bristol, Dept Elect & Elect Engn, Bristol BS8 1UB, Avon, England
来源：
IMAGE AND VISION COMPUTING | 2007年 / 25卷 / 08期
关键词：
particle filtering; tracking in video sequences; colour; texture; edges; multiple cues; Bhattacharyya distance;
D O I：
10.1016/j.imavis.2006.07.017
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents visual cues for object tracking in video sequences using particle filtering. A consistent histogram-based framework is developed for the analysis of colour, edge and texture cues. The visual models for the cues are learnt from the first frame and the tracking can be carried out using one or more of the cues. A method for online estimation of the noise parameters of the visual models is presented along with a method for adaptively weighting the cues when multiple models are used. A particle filter (PF) is designed for object tracking based on multiple cues with adaptive parameters. Its performance is investigated and evaluated with synthetic and natural sequences and compared with the mean-shift tracker. We show that tracking with multiple weighted cues provides more reliable performance than single cue tracking. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码：1217 / 1227
页数：11
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