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Spatiotemporal action localization requires the incorporation of two sourcesof information into the designed architecture: (1) temporal information fromthe previous frames and (2) spatial information from the key frame. Currentstate-of-the-art approaches usually extract these information with separatenetworks and use an extra mechanism for fusion to get detections. In this work,we present YOWO, a unified CNN architecture for real-time spatiotemporal actionlocalization in video streams. YOWO is a single-stage architecture with twobranches to extract temporal and spatial information concurrently and predictbounding boxes and action probabilities directly from video clips in oneevaluation. Since the whole architecture is unified, it can be optimizedend-to-end. The YOWO architecture is fast providing 34 frames-per-second on16-frames input clips and 62 frames-per-second on 8-frames input clips, whichis currently the fastest state-of-the-art architecture on spatiotemporal actionlocalization task. Remarkably, YOWO outperforms the previous state-of-the artresults on J-HMDB-21 and UCF101-24 with an impressive improvement of ~3% and~12%, respectively. Moreover, YOWO is the first and only single-stagearchitecture that provides competitive results on AVA dataset. We make our codeand pretrained models publicly available.

You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization