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Temporal Hockey Action Recognition via Pose and Optical Flows
[article]
2018
arXiv
pre-print
Recognizing actions in ice hockey using computer vision poses challenges due to bulky equipment and inadequate image quality. A novel two-stream framework has been designed to improve action recognition accuracy for hockey using three main components. First, pose is estimated via the Part Affinity Fields model to extract meaningful cues from the player. Second, optical flow (using LiteFlowNet) is used to extract temporal features. Third, pose and optical flow streams are fused and passed to
arXiv:1812.09533v1
fatcat:yc3vxgo2wvbljfa7wf46i7sd4a