An HMM/MRF-based stochastic framework for robust vehicle tracking
An HMM/MRF-based stochastic framework for robust vehicle tracking
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DOI：
10.1109/tits.2004.833791
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发表时间：
2004-09
影响因子：
8.5
通讯作者：
Jien Kato;Toyohide Watanabe;S. Joga;Y. Liu;H. Hase
中科院分区：
文献类型：
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作者：
Jien Kato;Toyohide Watanabe;S. Joga;Y. Liu;H. Hase
移动物体的阴影通常会阻碍稳健的视觉跟踪。在本文中，我们提出了一种基于隐马尔可夫模型/马尔可夫随机场（HMM/MRF）的分割方法的汽车跟踪器，该方法能够将图像的每个小区域分为三个不同的类别：车辆、车辆阴影和交通监控电影中的背景。一个小区域位置的不同类别的时间连续性被建模为沿着时间轴的单个 HMM，独立于邻近区域。为了将相邻区域之间的空间相关信息合并到跟踪过程中，在状态估计阶段，HMM的输出被视为MRF，并且结合MRF采用最大后验准则进行优化。在每个时间步，图像的状态估计相当于通过随机松弛过程生成的 MRF 的最优配置。实验结果表明，使用该方法可以高精度地区分前景（车辆）和非前景区域（包括移动车辆的阴影）。
Shadows of moving objects often obstruct robust visual tracking. In this paper, we present a car tracker based on a hidden Markov model/Markov random field (HMM/MRF)-based segmentation method that is capable of classifying each small region of an image into three different categories: vehicles, shadows of vehicles, and background from a traffic-monitoring movie. The temporal continuity of the different categories for one small region location is modeled as a single HMM along the time axis, independently of the neighboring regions. In order to incorporate spatial-dependent information among neighboring regions into the tracking process, at the state-estimation stage, the output from the HMMs is regarded as an MRF and the maximum a posteriori criterion is employed in conjunction with the MRF for optimization. At each time step, the state estimation for the image is equivalent to the optimal configuration of the MRF generated through a stochastic relaxation process. Experimental results show that, using this method, foreground (vehicles) and nonforeground regions including the shadows of moving vehicles can be discriminated with high accuracy.