Dynamic representation-based tracker for long-term pedestrian tracking with occlusion

This paper presents a dynamic representation-based tracker (DRT) to handle occlusions in the long-term pedestrian tracking of a single target. In our DRT, an adaptive representation network (ARN) is first constructed to extract multiple features, including classical features such as appearance and pose as well as some vector-format deep features. These features are then stacked to form a dynamic representation so as to convert the target tracking into a matching problem between the target features and candidate features, where the Euclidean distance (ED) and locality-constrained linear coding (LLC) are used as measurements in the decision-making. Next, the target state is determined through a voting procedure according to the feature matching error. Finally, a pose supervised module (PSM) and an IOU filtering module (IFM) are applied, respectively, to refine the target state and to filter out some invalid candidate targets that have been detected. Experimental results on public benchmark datasets show that our DRT is quite robust to complex environments with long-term pedestrian occlusions, and outperforms several existing state-of-the-arts trackers as it produces the best performance on both the pedestrian tracking dataset with occlusion (PTDO) and the pedestrian tracking dataset with occlusion plus (PTDO Plus).