Object tracking is an important procedure in the computer vision field as it estimates the position, size, and state of an object along the video’s timeline. Although many algorithms were proposed with high accuracy, object tracking in diverse contexts is still a challenging problem. The paper presents some methods to track the movement of two types of objects: arbitrary objects and humans. Both problems estimate the state density function of an object using particle filters. For the videos of a static or relatively static camera, we adjusted the state transition model by integrating the movement direction of the object. Also, we propose that partitioning the object needs tracking. To track the human, we partitioned the human into N parts and, then, tracked each part. During tracking, if a part deviated from the object, it was corrected by centering rotation, and the part was, then, combined with other parts.

中文翻译：

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基于多部分组合运动方向信息的多粒子滤波器跟踪目标


对象跟踪是计算机视觉领域的一个重要过程，因为它可以估计视频时间轴上对象的位置、大小和状态。尽管提出了许多高精度算法，但不同环境下的目标跟踪仍然是一个具有挑战性的问题。本文提出了一些跟踪两类物体运动的方法：任意物体和人类。这两个问题都使用粒子滤波器来估计对象的状态密度函数。对于静态或相对静态摄像机的视频，我们通过整合物体的运动方向来调整状态转换模型。此外，我们建议对需要跟踪的对象进行分区。为了跟踪人体，我们将人体分为N 个部分，然后跟踪每个部分。在跟踪过程中，如果某个部分偏离了物体，则通过居中旋转进行校正，然后将该部分与其他部分组合起来。