Exploiting textures for better action recognition in low-quality videos

Saimunur Rahman, John See*, Chiung Ching Ho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
17 Downloads (Pure)


Human action recognition is an increasingly matured field of study in the recent years, owing to its widespread use in various applications. A number of related research problems, such as feature representations, human pose and body parts detection, and scene/object context, are being actively studied. However, the general problem of video quality—a realistic issue in the face of low-cost surveillance infrastructure and mobile devices, has not been systematically investigated from various aspects. In this paper, we address the problem of action recognition in low-quality videos from a myriad of perspectives: spatial and temporal downsampling, video compression, and the presence of motion blurring and compression artifacts. To increase the resilience of feature representation in these type of videos, we propose to use textural features to complement classical shape and motion features. Extensive results were carried out on low-quality versions of three publicly available datasets: KTH, UCF-YouTube, HMDB. Experimental results and analysis suggest that leveraging textural features can significantly improve action recognition performance under low video quality conditions.

Original languageEnglish
Article number74
JournalEURASIP Journal on Image and Video Processing
Publication statusPublished - 21 Nov 2017


  • Action recognition
  • Low-quality video
  • Motion
  • Shape
  • Spatio-temporal features
  • Textures

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Electrical and Electronic Engineering


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