Abstract
Spatiotemporal action localization requires incorporation of two sources of information into the designed architecture: (1) Temporal information from the previous frames and (2) spatial information from the key frame. Current state-of-the-art approaches usually extract these information with separate networks and use an extra mechanism for fusion to get detections. In this work, we present YOWO, a unified CNN architecture for real-time spatiotemporal action localization in video stream. YOWO makes use of a single neural network to extract temporal and spatial information concurrently and predict bounding boxes and action probabilities directly from video clips in one evaluation. Since the whole architecture is unified, it can be optimized end-to-end. The YOWO architecture is fast providing 34 frames-per-second on 16-frames input clips and 62 frames-per-second on 8-frames input clips. Remarkably, YOWO outperforms the previous state-of-the art results on J-HMDB-21 (71.1%) and UCF101-24 (75.0%) with 74.4% and 87.2% frame-mAP, respectively.
Abstract (translated)
URL
https://arxiv.org/abs/1911.06644
