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An Efficient Human Instance-Guided Framework for Video Action Recognition

Sensors (Basel, Switzerland), 2021-12, Vol.21 (24), p.8309 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 by the authors. 2021 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s21248309 ;PMID: 34960404

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  • Title:
    An Efficient Human Instance-Guided Framework for Video Action Recognition
  • Author: Lee, Inwoong ; Kim, Doyoung ; Wee, Dongyoon ; Lee, Sanghoon
  • Subjects: Boxes ; Classification ; Computer vision ; convolutional neural network ; human action recognition ; Human Activities ; Human activity recognition ; human detection ; Human motion ; Humans ; Image contrast ; Metadata ; Motion ; Motion perception ; multiple human tracking ; Neural Networks, Computer ; Recognition, Psychology ; Sensors ; temporal sequence analysis ; Vision, Ocular
  • Is Part Of: Sensors (Basel, Switzerland), 2021-12, Vol.21 (24), p.8309
  • Description: In recent years, human action recognition has been studied by many computer vision researchers. Recent studies have attempted to use two-stream networks using appearance and motion features, but most of these approaches focused on clip-level video action recognition. In contrast to traditional methods which generally used entire images, we propose a new human instance-level video action recognition framework. In this framework, we represent the instance-level features using human boxes and keypoints, and our action region features are used as the inputs of the temporal action head network, which makes our framework more discriminative. We also propose novel temporal action head networks consisting of various modules, which reflect various temporal dynamics well. In the experiment, the proposed models achieve comparable performance with the state-of-the-art approaches on two challenging datasets. Furthermore, we evaluate the proposed features and networks to verify the effectiveness of them. Finally, we analyze the confusion matrix and visualize the recognized actions at human instance level when there are several people.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s21248309
    PMID: 34960404
  • Source: Geneva Foundation Free Medical Journals at publisher websites
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    ROAD: Directory of Open Access Scholarly Resources
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