Enhanced GM-PHD Filter Using CNN-Based Weight Penalization for Multi-Target Tracking

摘要

In this paper, an enhanced Gaussian mixture probability hypothesis density filter (GM-PHD) using convolutional neural network (CNN) based weight penalization is proposed to track multiple targets in video. Existing GM-PHD filter based tracking methods are not always able to accurately track the targets when they are in close proximity, especially with noisy detection responses or in a crowded environments. To address this issue, a measurement classification step which combines a confidence score with a gating technique is presented to discard the false measurements and initialise new-born targets. High level human features extracted from a pre- trained CNN are utilized to penalize the ambiguous weights in the weight matrix. In addition, we integrate an improved track management scheme with occlusion handling to form the tracks of confirmed targets and maintain the track continuity. Experimental results on two publicly available benchmark video sequences validate the efficacy of our proposed method in video-based multi-target tracking.