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Recognizing actions in ice hockey using computer vision poses challenges dueto bulky equipment and inadequate image quality. A novel two-stream frameworkhas been designed to improve action recognition accuracy for hockey using threemain components. First, pose is estimated via the Part Affinity Fields model toextract meaningful cues from the player. Second, optical flow (usingLiteFlowNet) is used to extract temporal features. Third, pose and optical flowstreams are fused and passed to fully-connected layers to estimate the hockeyplayer's action. A novel publicly available dataset named HARPET (Hockey ActionRecognition Pose Estimation, Temporal) was created, composed of sequences ofannotated actions and pose of hockey players including their hockey sticks asan extension of human body pose. Three contributions are recognized. (1) Thenovel two-stream architecture achieves 85% action recognition accuracy, withthe inclusion of optical flows increasing accuracy by about 10%. (2) The uniquelocalization of hand-held objects (e.g., hockey sticks) as part of poseincreases accuracy by about 13%. (3) For pose estimation, a bigger and moregeneral dataset, MSCOCO, is successfully used for transfer learning to asmaller and more specific dataset, HARPET, achieving a PCKh of 87%.

Temporal Hockey Action Recognition via Pose and Optical Flows