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Spatio-temporal action recognition has been a challenging task that involvesdetecting where and when actions occur. Current state-of-the-art actiondetectors are mostly anchor-based, requiring sensitive anchor designs and hugecomputations due to calculating large numbers of anchor boxes. Motivated bynascent anchor-free approaches, we propose Point3D, a flexible andcomputationally efficient network with high precision for spatio-temporalaction recognition. Our Point3D consists of a Point Head for actionlocalization and a 3D Head for action classification. Firstly, Point Head isused to track center points and knot key points of humans to localize thebounding box of an action. These location features are then piped into atime-wise attention to learn long-range dependencies across frames. The 3D Headis later deployed for the final action classification. Our Point3D achievesstate-of-the-art performance on the JHMDB, UCF101-24, and AVA benchmarks interms of frame-mAP and video-mAP. Comprehensive ablation studies alsodemonstrate the effectiveness of each module proposed in our Point3D.

Point3D: tracking actions as moving points with 3D CNNs