Human body joint nodes detection is a considerably challenging task which has drawn enormous attention in the field of computer vision recently. The challenges of this task include: coping with the complex structure of human body joints, denoting the interdependence between joint nodes, and dealing with the sheltered and overlapped body joint nodes. Among the common solutions to this task, the models based on deep learning are widely applied and provide useful results. However, the existing models have following drawbacks: 1) comparatively low accuracy in prediction; 2) poor performance in multi-objective tasks. In our work, we proposed a novel method aiming at more satisfactory results. We firstly detect the relevant regions of human body with Faster RCNN, and then input the regions into a modified DeepPose algorithm. We achieve the state-of-the-art results in the detection of the wrist and knee on MPII dataset, improving 1.2% and 0.3% in PCKh, respectively. The total PCKh is 87.6% on MPII dataset.
YU Baoling
,
YU Songkun
,
SUN Yaoran
,
YANG Zhen
,
FU Xubo
. Human body joint nodes detection based on DeepPose and Faster RCNN[J]. Journal of University of Chinese Academy of Sciences, 2020
, 37(6)
: 828
-834
.
DOI: 10.7523/j.issn.2095-6134.2020.06.015
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