Abstract
Human keypoints are effective human pose descriptions. Human behavior can be recognized by the motion of keypoints of human bodies. In this paper, we propose a method, which is based on a PAFs approach, for human keypoints detection. The proposed method makes improvements in two aspects: (1) It perfects the joint points matching algorithm by re-matching. (2) It uses multi-branch PAFs to correct the keypoint connections, thus improving the wrong connection problem of upper and lower limbs for multi-person keypoint detection. The improved PAFs method, whose mAP reaches 53.6% on HKD dataset, improved the score of the original method by 2%.
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Acknowledgment
Research supported by NVIDIA Corporation with the donation of the Quadro P5000 GPU, National High-Tech Research and Development Plan under Grant 2015AA042306 and Beijing Little Wheel research project.
This work was supported partly by the Innovation Leading Action of Suzhou-Tsinghua (No. 016SZ0219).
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Guo, M., Gao, Y., Wang, B., Sun, F. (2019). Development of Multi-person Pose Estimation Method Based on PAFs. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-7986-4_7
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