一种基于Multi-Egocentric视频运动轨迹重建的多目标跟踪算法

Egocentric视频具有目标运动剧烈、遮挡频繁、目标尺度差异明显及视角时变性强的特点，给目标跟踪任务造成了极大的困难。本文从重建不同视角Egocentric视频中各目标的运动轨迹出发，提出一种基于Multi-Egocentric视频运动轨迹重建的多目标跟踪算法，该方法基于多视角同步帧之间的单应性约束解决目标遮挡和丢失问题，然后根据多视角目标空间位置约束关系通过轨迹重建进一步优化目标定位，并采用卡尔曼滤波构建目标运动模型优化目标运动轨迹，在BJMOT、EPLF-campus4数据集上的对比实验验证了本文算法在解决Multi-Egocentric视频多目标跟踪轨迹不连续问题的有效性。

A multi-object tracking algorithm based on trajectory reconstruction on multi-egocentric video

In egocentric video, objects have the characteristics of violent motion, frequent occlusion, so it brings much trouble to carrying out the tracking task. In this paper, we propose a multi-object tracking algorithm based on the motion trajectory reconstruction of multi-egocentric video from different visual angles egocentric videos. First, this method is based on the homographic constraint of multi-view synch frames to fix position of occluded and missing object. Second, using the relative position constraint relation of multi-angle target, the trajectory is reconstructed to locate the target position. Meanwhile, the trajectory of the object is optimized by constructing the motion model of object. Then the continuous trajectory of the object is obtained and the problem of the discontinuity trajectory in multi-object tracking is resolved. In the end, the performance of proposed method has been verified by using the multi-view video dataset which is created by us.