Detecting Parts for Action Localization

Nicolas Chesneau 1 Grégory Rogez 1 Karteek Alahari 1 Cordelia Schmid 1
1 Thoth - Apprentissage de modèles à partir de données massives
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann
Abstract : In this paper, we propose a new framework for action localization that tracks people in videos and extracts full-body human tubes, i.e., spatio-temporal regions localizing actions, even in the case of occlusions or truncations. This is achieved by training a novel human part detector that scores visible parts while regressing full-body bounding boxes. The core of our method is a convolutional neural network which learns part proposals specific to certain body parts. These are then combined to detect people robustly in each frame. Our tracking algorithm connects the image detections temporally to extract full-body human tubes. We apply our new tube extraction method on the problem of human action localization, on the popular JHMDB dataset, and a very recent challenging dataset DALY (Daily Action Localization in YouTube), showing state-of-the-art results.
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Communication dans un congrès
BMVC - British Machine Vision Conference, Sep 2017, London, United Kingdom. 2017
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https://hal.inria.fr/hal-01573629
Contributeur : Thoth Team <>
Soumis le : jeudi 10 août 2017 - 10:52:48
Dernière modification le : vendredi 11 août 2017 - 11:33:11

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  • HAL Id : hal-01573629, version 1
  • ARXIV : 1707.06005

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Nicolas Chesneau, Grégory Rogez, Karteek Alahari, Cordelia Schmid. Detecting Parts for Action Localization. BMVC - British Machine Vision Conference, Sep 2017, London, United Kingdom. 2017. <hal-01573629>

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