Robust multi-object tracking with semantic color correlation

Multi-object tracking is an important computer vision task with wide variety of real-life applications from surveillance and monitoring to biomedical video analysis. Multi-object tracking is a challenging problem due to complications such as partial or full occlusions, factors affecting object appearance, object interaction dynamics, etc. and computational cost. In this paper, we propose a detection-based multi-object tracking system that uses a two-step data association scheme to ensure time efficiency while preserving tracking accuracy; a robust but discriminative object appearance model that compares object color attributes using a novel color correlation cost matrix; and a framework that handles occlusions through prediction. Our experiments on UA-DETRAC multi-object tracking benchmark dataset consisting of challenging real-world traffic videos show promising results against state-of-the-art trackers.