Are current monocular computer vision systems for human action recognition suitable for visual surveillance applications?

  • Authors:

  • Jean-Christophe Nebel;Michał Lewandowski;Jérôme Thévenon;Francisco Martínez;Sergio Velastin

  • Affiliations:

  • Digital Imaging Research Centre, Kingston University, London, Kingston-Upon-Thames, UK;Digital Imaging Research Centre, Kingston University, London, Kingston-Upon-Thames, UK;Digital Imaging Research Centre, Kingston University, London, Kingston-Upon-Thames, UK;Digital Imaging Research Centre, Kingston University, London, Kingston-Upon-Thames, UK;Digital Imaging Research Centre, Kingston University, London, Kingston-Upon-Thames, UK

  • Venue:
  • ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
  • Year:
  • 2011

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Abstract

Since video recording devices have become ubiquitous, the automated analysis of human activity from a single uncalibrated video has become an essential area of research in visual surveillance. Despite variability in terms of human appearance and motion styles, in the last couple of years, a few computer vision systems have reported very encouraging results. Would these methods be already suitable for visual surveillance applications? Alas, few of them have been evaluated in the two most challenging scenarios for an action recognition system: view independence and human interactions. Here, first a review of monocular human action recognition methods that could be suitable for visual surveillance is presented. Then, the most promising frameworks, i.e. methods based on advanced dimensionality reduction, bag of words and random forest, are described and evaluated on IXMAS and UT-Interaction datasets. Finally, suitability of these systems for visual surveillance applications is discussed.