Identifying motion pathways in highly crowded scenes: A non-parametric tracklet clustering approach

Many approaches that address the analysis of crowded scenes rely on using short trajectory fragments, also known as tracklets, of moving objects to identify motion pathways. Typically, such approaches aim at defining meaningful relationships among tracklets. However, defining these relationships and incorporating them in a crowded scene analysis framework is a challenge. In this article, we introduce a robust approach to identifying motion pathways based on tracklet clustering. We formulate a novel measure, inspired by line geometry, to capture the pairwise similarities between tracklets. For tracklet clustering, the recent distance dependent Chinese restaurant process (DD-CRP) model is adapted to use the estimated pairwise tracklet similarities. The motion pathways are identified based on two hierarchical levels of DD-CRP clustering such that the output clusters correspond to the pathways of moving objects in the crowded scene. Moreover, we extend our DD-CRP clustering adaptation to incorporate the source and sink gate probabilities for each tracklet as a high-level semantic prior for improving clustering performance. For qualitative evaluation, we propose a robust pathway matching metric, based on the chi-square distance, that accounts for both spatial coverage and motion orientation in the matched pathways. Our experimental evaluation on multiple crowded scene datasets, principally, the challenging Grand Central Station dataset, demonstrates the state-of-the-art performance of our approach. Finally, we demonstrate the task of motion abnormality detection, both at the tracklet and frame levels, against the normal motion patterns encountered in the motion pathways identified by our method, with competent quantitative performance on multiple datasets.