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This paper presents a computationally efficient approach for temporal action detection in untrimmed videos that outperforms state-of-the-art methods by a large margin. We exploit the temporal structure of actions by modeling an action as a sequence of sub-actions. A novel and fully automatic sub-action discovery algorithm is proposed, where the number of sub-actions for each action as well as their types are automatically determined from the training videos. We find that the discovered sub-actions are semantically meaningful. To localize an action, an objective function combining appearance, duration and temporal structure of sub-actions is optimized as a shortest path problem in a network flow formulation. A significant benefit of the proposed approach is that it enables real-time action localization (40 fps) in untrimmed videos. We demonstrate state-of-the-art results on THUMOS’14 and MEXaction2 datasets.

Real-Time Temporal Action Localization in Untrimmed Videos by Sub-Action Discovery