Rewind to track: Parallelized apprenticeship learning with backward tracklets
Jiang Liu, Jia Chen, De Cheng, Chenqiang Gao, Alexander G. Hauptmann · 2017
Data association, which could be categorized into offline approaches and the online counterparts, is a crucial part of a multi-object tracker in the tracking-by-detection framework. On the one hand, classical offline data association methods exploit all the video data and have high computation cost, which makes them unscalable to long-term offline video data. On the other hand, online approaches have much lower computation cost, but they suffer from ID-switches and tracklet drifting problem when directly applied to offline data as they are only aware of “past” observations. In this paper, we propose a mixed style tracker, which is not only as efficient as the online tracker but also aware of “future” observations in offline setting. We start from a Markov Decision Process (MDP) online tracker and design a parallelized apprenticeship learning algorithm to learn both the reward function and transition policy in MDP. By proposing a rewind to track strategy to generate backward tracklets, future detections in offline data are efficiently utilized to obtain a more stable similarity measurement for association. Experiment results show that our approach achieves the state-of-the-art performance on challenging datasets.