Abstract
Multi-person articulated pose tracking is a newly proposed computer vision task which aims at associating corresponding person articulated joints to establish pose trajectories. In this paper, we propose a region-based deep appearance model combined with an LSTM pose model to measure the similarity between different identities. A novel hierarchical association method is proposed to reduce the time consumption for deep feature extraction. We divide the association procedure into two stages and extract deep feature only when the pairs of identities are difficult to distinguish. Extensive experiments are conducted on the newly released multi-person pose tracking benchmark: PoseTrack. The results show that the tracking accuracy gains an obvious improvement when adopting multiple association cues, and the hierarchical association method could improve the tracking speed obviously.
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This work is supported by National High-Tech R&D Program (863 Program) under Grant 2015AA016402.
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Xu, C., Zhou, Y. (2018). Hierarchical Online Multi-person Pose Tracking with Multiple Cues. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_27
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