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Modeling Spatio-Temporal Human Track Structure for Action Localization

Abstract : This paper addresses spatio-temporal localization of human actions in video. In order to localize actions in time, we propose a recurrent localization network (RecLNet) designed to model the temporal structure of actions on the level of person tracks. Our model is trained to simultaneously recognize and localize action classes in time and is based on two layer gated recurrent units (GRU) applied separately to two streams, i.e. appearance and optical flow streams. When used together with state-of-the-art person detection and tracking, our model is shown to improve substantially spatio-temporal action localization in videos. The gain is shown to be mainly due to improved temporal localization. We evaluate our method on two recent datasets for spatio-temporal action localization, UCF101-24 and DALY, demonstrating a significant improvement of the state of the art.
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Preprints, Working Papers, ...
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Contributor : Guilhem Chéron Connect in order to contact the contributor
Submitted on : Sunday, January 13, 2019 - 2:36:01 PM
Last modification on : Friday, January 21, 2022 - 3:17:24 AM

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  • HAL Id : hal-01979583, version 1
  • ARXIV : 1806.11008



Guilhem Chéron, Anton Osokin, Ivan Laptev, Cordelia Schmid. Modeling Spatio-Temporal Human Track Structure for Action Localization. 2019. ⟨hal-01979583⟩



Les métriques sont temporairement indisponibles