Simultaneous Trajectory Association and Clustering for Motion Segmentation
Yuxi Wang, Yue Liu, Erik Blasch, Haibin Ling · IEEE Signal Processing Letters · 2017
Trajectory association and clustering are two key problems in motion analysis. While association links the points of interest to form trajectories, clustering discovers motion patterns of these trajectories and group them into clusters. Despite mutually related, the two problems have been typically studied separately in the literature. In this letter, we formulate them as a unified optimization problem and take the advantage of high-order information to capture the interrelations for Motion Segmentation by Trajectory Association and Clustering (MSTAC). To solve this unified problem, we propose an alternating optimization strategy to improve the association and clustering in each iteration. Specifically, a tensor-based multidimensional assignment method with high-order motion context information is proposed for trajectory association; and a minimum cost multicut-based trajectory clustering method is introduced for trajectory clustering. While the association process provides incomplete trajectories to clustering, the clustering method presents high-order context information to improve the performance of association; and thus they benefit from each other. Experiments on the Hopkins 155 dataset and a realistic airport sequence demonstrate that the proposed MSTAC framework obtains high accuracy on both trajectory association and clustering.