Abstract

Existing video-based human pose estimation methods typically adopt large networks to perform body joints localization on all frames. Despite of impressive accuracy performance, the relatively high memory and computation requirements significantly burden their applicability on resource-constraint systems (e.g., embedded devices). To solve this issue, this paper proposes a novel yet effective lightweight framework, called FVPE, for fast and accurate human pose estimation in videos. Specifically, FVPE adopts the knowledge distillation (KD) strategy to train a small pose estimator network, which is capable of executing rapidly with low computational cost. To increase the overall efficiency, FVPE exploits the temporal coherence between successive video frames and explicitly propagates body joints from previous frames rather than naively extracting them using a pose estimator. Furthermore, FVPE introduces an online key-frame selection scheme to decide whether the current pose should be calculated by the pose estimator or be propagated from the previous key-frame, being able to flexibly deal with video sequences with different length, frame rate, pose complexity, etc.. Experiments on Penn Action and Sub-JHMDB datasets demonstrate that the proposed method achieves comparative accuracy, but with substantial speed-up.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.