Papers › A Pursuit of Temporal Accuracy in General Activity Detection

A Pursuit of Temporal Accuracy in General Activity Detection

8 Mar 2017arXiv:1703.02716archive 2025-07-28

Yuanjun Xiong, Yue Zhao, Li-Min Wang, Dahua Lin, Xiaoou Tang

Detecting activities in untrimmed videos is an important but challenging task. The performance of existing methods remains unsatisfactory, e.g., they often meet difficulties in locating the beginning and end of a long complex action. In this paper, we propose a generic framework that can accurately detect a wide variety of activities from untrimmed videos. Our first contribution is a novel proposal scheme that can efficiently generate candidates with accurate temporal boundaries. The other contribution is a cascaded classification pipeline that explicitly distinguishes between relevance and completeness of a candidate instance. On two challenging temporal activity detection datasets, THUMOS14 and ActivityNet, the proposed framework significantly outperforms the existing state-of-the-art methods, demonstrating superior accuracy and strong adaptivity in handling activities with various temporal structures.

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