GM-PHD FILTER BASED ONLINE MULTIPLE HUMAN TRACKING USING DEEP DISCRIMINATIVE CORRELATION MATCHING

In this paper, we propose deep discriminative correlation matching within the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter for online multiple human tracking. In this matching scheme, we mainly exploit the Convolutional Neural Network (CNN) based Discriminative Correlation Filter (DCF) as a target-specific classifier to discriminate the desired target from background and remaining targets. DCFs are learned through the extracted features obtained from the outputs of the last convolutional layers which are capable to encode the target appearances with better discriminativity and robustness to appearance changes. Moreover, we present a hybrid likelihood function that fuses the spatio-temporal relation and correlation matching score to collaboratively enhance the PHD association step. Experimental results on the MOT17 Challenge benchmark [1] confirm the improved performance of our proposed method as compared with other state-of-the-art techniques.