Occlusion-robust Online Multi-object Visual Tracking using a GM-PHD Filter with a CNN-based Re-identification

We propose a novel online multi-object visual tracking algorithm via a tracking-by-detection paradigm using a Gaussian mixture Probability Hypothesis Density (GM-PHD) filter and deep Convolutional Neural Network (CNN) appearance representations learning. The GM-PHD filter has a linear complexity with the number of objects and observations while estimating the states and cardinality of unknown and time-varying number of objects in the scene. Though it handles object birth, death and clutter in a unified framework, it is susceptible to miss-detections and does not include the identity of objects. We use visual-spatio-temporal information obtained from object bounding boxes and deeply learned appearance representations to perform estimates-to-tracks data association for labelling of each target. We learn the deep CNN appearance representations by training an identification network (IdNet) on large-scale person re-identification data sets. We also employ additional unassigned tracks prediction after the update step to overcome the susceptibility of the GM-PHD filter towards miss-detections caused by occlusion. Our tracker which runs in real-time is applied to track multiple objects in video sequences acquired under varying environmental conditions and objects density. Lastly, we make extensive evaluations on Multiple Object Tracking 2016 (MOT16) and 2017 (MOT17) benchmark data sets and find out that our online tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy and identification.