Sequential Monte Carlo tracking by fusing multiple cues in video sequences
Sequential Monte Carlo tracking by fusing multiple cues in video sequences
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DOI：
10.1016/j.imavis.2006.07.017
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发表时间：
2007-08-01
影响因子：
4.7
通讯作者：
Canagarajah, Nishan
中科院分区：
文献类型：
--
作者：
Brasnett, Paul;Mihaylova, Lyudmila;Canagarajah, Nishan
本文提出了使用粒子滤波在视频序列中进行对象跟踪的视觉线索。开发了一个一致的基于直方图的框架，用于分析颜色、边缘和纹理线索。提示的视觉模型是从第一帧中学习的，并且可以使用一个或多个提示来执行跟踪。提出了一种在线估计视觉模型的噪声参数的方法以及一种在使用多个模型时自适应地加权线索的方法。粒子滤波器 (PF) 设计用于基于具有自适应参数的多个线索的对象跟踪。使用合成和天然序列研究和评估其性能，并与均值漂移跟踪器进行比较。我们表明，使用多个加权线索进行跟踪比单个线索跟踪提供更可靠的性能。 (c) 2006 Elsevier B.V. 保留所有权利。
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.