Multi-object Tracking Cascade with Multi-Step Data Association and Occlusion Handling

Multi-object tracking is a fundamental computer vision task with a wide variety of real-life applications ranging from surveillance and monitoring to biomedical video analysis. Multi-object tracking is a challenging task due to complications caused by object appearance changes, complex object dynamics, clutter in the environment, and partial or full occlusions. In this paper, we propose a time-efficient detection-based multi-object tracking system using a three-step cascaded data association scheme that combines a fast spatial distance only short-term data association, a robust tracklet linking step using discriminative object appearance models, and an explicit occlusion handling unit relying not only on tracked objects' motion patterns but also on environmental constraints such as presence of potential occluders in the scene. 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.