基于卡尔曼滤波的室外环境中人跟踪

M. Mirabi, S. Javadi
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引用次数: 19

摘要

动态场景下的人体跟踪一直是一个重要的研究课题。当在视频序列中捕捉到物体时,世界中的物体表现出复杂的相互作用,一些相互作用表现为遮挡。视觉跟踪系统必须能够跟踪部分或完全被遮挡的物体。在本文中,我们提出了一种动态场景中多目标跟踪的方法来处理目标部分遮挡。该算法分为两步,第一步我们使用高斯混合模型作为一种有效的方法从视频序列中提取运动目标。然后使用组合方法去除阴影。该算法的第二步是基于卡尔曼滤波的目标跟踪框架,该框架采用稳定婚姻问题(SMP)实现算法,该算法适用于进行数据关联和遮挡检测,并获得快速的平均位移计算结果,用于遮挡期间的跟踪。该方法具有成本低、复杂度低的优点,可以在实时系统中实现,并在多个真实数据集上进行了测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
People Tracking in Outdoor Environment Using Kalman Filter
Human tracking in dynamic scenes has been an important topic of research. Objects in the world exhibit complex interactions when captured in a video sequence, some interactions manifest themselves as occlusions. A visual tracking system must be able to track objects which are partially or even fully occluded. In this paper, we propose an approach for tracking multiple objects in dynamic scenes to handle objects partial occlusion. The algorithm consists of two steps at first step we use Gaussian Mixture Model as an effective way to extract moving objects from a video sequence. Then a combinational method is used for shadow removal. The second step of the proposed algorithm is object tracking framework based on Kalman filtering which uses Stable Marriage Problem (SMP) implemented algorithm which is adapted to perform data association and occlusion detection, and fast mean shift computation results for tracking during occlusion . Our approach has the advantages of low cost and low complexity, and can be realized in real time system and is tested on using several real world datasets.
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