Abstract
In the paper, we propose the visually similar k-poselets to recognize human poses (e.g., stoop, squat) in still images. Compared with the original k-poselets that are collected according to similar keypoints configurations, we further introduce appearance similarity constraints to generate visually similar k-poselets. The number of selected visually similar k-poselets for each pose category is iteratively decreased based on discriminative criterion. The pose dictionary, constructed with learned visually similar and discriminative k-poselets of different poses, is applied in pose recognition. The experimental results on our released human pose database verify the effectiveness of the proposed visually similar k-poselets based pose recognition method.
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Acknowledgement
This work was partly funded by NSFC (No. 61571297, No. 61527804, No. 61420106008), 111 Project (B07022), and China National Key Technology R&D Program (No. 2012BAH07B01).
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Ni, S., Liu, W., Cheng, H., Zhang, C. (2017). Visually Similar K-poselets Based Human Pose Recognition. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_31
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DOI: https://doi.org/10.1007/978-3-319-54526-4_31
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