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

Abstract

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.

Keywords

• labeled multi-bernoulli filter; tracking-by-detection; feature extraction; target reidentification