Actions speak louder than words: Searching human action video based on body movement

Yan Ching Lin*, Min Chun Hu, Wen-Huang Cheng, Yung Huan Hsieh, Hong Ming Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Human action video search is a frequent demand in multimedia applications, and conventional video search schemes based on keywords usually fail to correctly find relevant videos due to noisy video tags. Observing the widespread use of Kinect-like depth cameras, we propose to search human action videos by directly performing the target action with body movements. Human actions are captured by Kinect and the recorded depth information is utilized to measure the similarity between the query action and each human action video in the database. We use representative depth descriptors without learning optimization to achieve real-time and promising performance as compatible as those of the leading methods based on color images and videos. Meanwhile, a large Depth-included Human Action video dataset, namely DHA, is collected to prove the effectiveness of the proposed video search system.

Original languageEnglish
Title of host publicationMM 2012 - Proceedings of the 20th ACM International Conference on Multimedia
Pages1261-1262
Number of pages2
DOIs
StatePublished - 26 Dec 2012
Event20th ACM International Conference on Multimedia, MM 2012 - Nara, Japan
Duration: 29 Oct 20122 Nov 2012

Publication series

NameMM 2012 - Proceedings of the 20th ACM International Conference on Multimedia

Conference

Conference20th ACM International Conference on Multimedia, MM 2012
CountryJapan
CityNara
Period29/10/122/11/12

Keywords

  • depth information
  • human action recognition
  • human action video retrieval

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