A method for joint detection and re-identification in multi-object tracking

Abstract

In order to better balance the detection accuracy and tracking speed, we propose an online balanced multi-object tracking method (BalMOT), which integrates object detection and appearance extraction into a single network, and can simultaneously output detection and appearance embedding. We also model the training of classification, regression, and embedding features as a multi-task training problem and each part is weighted based on the task-independent uncertainty method. In addition, we introduce the transition layer to optimize the repeated gradient information in the network and reduce the training cost. Through the training, our BalMOT system reaches 71.9% multiple object tracking accuracy (MOTA) on the MOT17 challenge dataset, and the speed fluctuates between 17.4 ~ 22.3 frames per second (FPS) according to the size of the input image.