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The recent development of commodity 360$^{\circ}$ cameras have enabled asingle video to capture an entire scene, which endows promising potentials insurveillance scenarios. However, research in omnidirectional video analysis haslagged behind the hardware advances. In this work, we address the importantproblem of action recognition in top-view 360$^{\circ}$ videos. Due to the widefiled-of-view, 360$^{\circ}$ videos usually capture multiple people performingactions at the same time. Furthermore, the appearance of people are deformed.The proposed framework first transforms omnidirectional videos into panoramicvideos, then it extracts spatial-temporal features using region-based 3D CNNsfor action recognition. We propose a weakly-supervised method based onmulti-instance multi-label learning, which trains the model to recognize andlocalize multiple actions in a video using only video-level action labels assupervision. We perform experiments to quantitatively validate the efficacy ofthe proposed method and qualitatively demonstrate action localization results.To enable research in this direction, we introduce 360Action, the firstomnidirectional video dataset for multi-person action recognition.

Weakly-Supervised Multi-Person Action Recognition in 360$^{\circ}$ Videos