基于时空轨迹关联的多人跟踪

Weizhi Nie, Anan Liu, Yuting Su
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引用次数: 19

摘要

在视频监控领域,多目标跟踪是一个具有挑战性的问题。本文提出了一种基于时空轨迹关联的多目标跟踪方法。首先,通过相邻帧中目标定位结果的逐帧关联,生成可靠的轨迹片段,即单个目标运动的整个轨迹碎片;为了避免遮挡对轨迹生成的负面影响,进行了基于部分的相似度计算。其次,考虑空间和时间约束,将生成的轨迹关联起来,输出个人的整个轨迹;特别地,我们将时空多轨迹匹配任务表述为具有时空上下文约束的马尔可夫链形式的最大a后验(MAP)问题。在pet 2012数据集上的实验验证了该方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiple Person Tracking by Spatiotemporal Tracklet Association
In the field of video surveillance, multiple object tracking is a challenging problem in the real application. In this paper, we propose a multiple object tracking method by spatiotemporal tracklet association. Firstly, reliable tracklets, the fragments of the entire trajectory of individual object movement, are generated by frame-wise association between object localization results in the neighbor frames. To avoid the negative influence of occlusion on reliable tracklet generation, part-based similarity computation is performed. Secondly, the produced tracklets are associated considering both spatial and temporal constrains to output the entire trajectory for individual person. Especially, we formulate the task of spatiotemporal multiple tracklet matching into a Maximum A Posterior (MAP) problem in the form of Markov Chain with spatiotemporal context constraints. The experiment on PETS 2012 dataset demonstrates the superiority of the proposed method.
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