Human Tracking with Particle Filter Based on Locally Adaptive Appearance Model
Human Tracking with Particle Filter Based on Locally Adaptive Appearance Model
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DOI：
10.2299/jsp.18.229
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Sangeun Lee;K. Horio
中科院分区：
文献类型：
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作者：
Sangeun Lee;K. Horio
在以前的工作中，我们提出了一种基于可靠外观模型(RAM)的人体跟踪算法。随机存储器是一组可区分的局部图像描述符，通过Boosting算法选择来识别初始帧中的目标，并被用作粒子fi之后的观测模型。由于人体跟踪中目标的外观模型会随着时间的推移而随着姿态的变化而不断变化，因此需要自适应地更新RAM以提高跟踪精度。在本文中，如果有必要，更新了一个不充分的fi局部图像描述子来进行稳健跟踪。为了在跟踪过程中对局部图像描述符是否合适进行分类，构造了与局部图像描述符对应的距离直方图。当直方图指示局部图像描述符缺乏跟踪性能时，则对其进行更新。实验结果表明，即使运动员的姿势经常发生变化，自适应外观模型也能成功地跟踪运动员。
In previous work, we proposed a human tracking algorithm based on the reliable appearance model (RAM). The RAM is a set of discriminative local image descriptors that is selected by a boosting algorithm to identify a target in the initial frame, and is employed as an observation model in a particle filter. As the appearance model of the target in human tracking constantly changes as time passes owing to changes in pose, it is necessary to adaptively update the RAM to improve the tracking accuracy. In this paper, if necessary, an insufficient local image descriptor for robust tracking is updated. In order to classify whether local image descriptors are suitable or not during tracking, a distance histogram corresponding to a local image descriptor is constructed. When the histogram indicates that the local image descriptor is lacking in tracking performance, then it is updated. The experimental results demonstrate that the adaptive appearance model successfully tracks sport players even when their pose often changes.