检测优化的标签多伯努利视频多目标跟踪算法

Detection Optimized Labeled Multi-Bernoulli Algorithm for Visual Multi-target Tracking

在检测器与跟踪器结合的视频多目标跟踪算法中,检测器好坏将直接影响整个跟踪算法性能,尤其是检测器的漏检以及误检,会导致目标的漏跟以及误跟,增加碎片化轨迹以及身份标签变换次数增加的问题。针对这些问题,在标签多伯努利的滤波框架下,设计了新的量测驱动新生目标识别方法以更快速精准地捕获新生目标。设计了目标重识别方法,结合标签多伯努利算法能够在短时间内维持标签的不变性,减少了碎片化轨迹及标签跳变数。引入新的模板选取策略,以避免将被遮挡的目标加入到模板中污染模板。考虑到标签多伯努利滤波为在线推理算法,采用了并行化加快算法的运算效率。结果表明,在标签多伯努利的框架下,提出算法能够有效解决标签跳变以及目标被遮挡无法准确跟踪的问题,在具有挑战性的MOT17数据集上进行测试,与其他相关滤波方法进行比较,具有不错的跟踪效果。

In a video multi-target tracking algorithm combining a detector with a tracker,the quality of detector affects the performance of the whole tracking algorithm.The missing and false detection will lead to the missing and false tracking of target,increase the fragmentation trajectory and increase the number of identity tag transformation.In order to solve these problems,this paper further optimizes the tracking algorithm in the framework of labeled multi-Bernoulli filter,designs a new measurement driven newborn target recognition method to capture newborn targets more quickly and accurately,designs a new target recognition method which can maintain the label invariance in a short time and reduce the fragmentation trajectory and label jumping,and introduces a new template selection strategy to avoid polluting the template by adding the occluded target to the template.Considering the labeled multi-Bernoulli filter is an online reasoning algorithm,parallelization is adopted to speed up the operation efficiency of the algorithm.The result shows that the proposed algorithm can effectively solve the problems of label jumping and inaccurate tracking by target occlusion.It is tested on the challenging MOT17 dataset and has good tracking effect compared with other relevant filtering methods.