Training Physical and Geometrical Mid-Points for Multi-person Pose Estimation and Human Detection Under Congestion and Low Resolution
Training Physical and Geometrical Mid-Points for Multi-person Pose Estimation and Human Detection Under Congestion and Low Resolution
复制标题
训练物理和几何中点以实现拥塞和低分辨率下的多人姿势估计和人体检测
DOI：
10.1007/s42979-020-00217-9
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Shoji Nishimura
中科院分区：
文献类型：
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作者：
Yadong Pan;Ryo Kawai;Noboru Yoshida;Hiroo Ikeda;Shoji Nishimura
本文介绍了用于多人姿态估计和人体检测的NeoPose算法的设计和性能评估。NeoPose的设计针对的是拥堵情况下和图像低分辨率下的人体检测问题。在这种情况下，我们比较了不同版本的NeoPose算法以及其他现有算法在人类检测任务中的性能。在整个任务中，讨论了两种中点(物理中点和几何中点)和反卷积结构的有用性。实验结果表明，采用几何中点和反卷积结构的NeoPose在准确率和召回率方面都表现出最好的评价效果。
This paper introduces the design and evaluation of NeoPose which is developed for multi-person pose estimation and human detection. The design of NeoPose is targeting the issue of human detection under congested situation and with low resolution in the image. Under such situations, we compared the performance of different versions of NeoPose as well as other existing algorithms in a human detection task. Throughout the task, the usefulness of two kinds of mid-point (physical and geometrical mid-points) and a deconvolution structure was discussed. Experiment results indicated that NeoPose which applied geometrical mid-points and deconvolution structure performed the best in terms of both precision and recall in the evaluation.