A review of human activity recognition methods

Michalis Vrigkas, Christophoros Nikou, Ioannis A. Kakadiaris

Research output: Contribution to journalReview articlepeer-review

386 Scopus citations


Recognizing human activities from video sequences or still images is a challenging task due to problems, such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance. Many applications, including video surveillance systems, human-computer interaction, and robotics for human behavior characterization, require a multiple activity recognition system. In this work, we provide a detailed review of recent and state-of-the-art research advances in the field of human activity classification. We propose a categorization of human activity methodologies and discuss their advantages and limitations. In particular, we divide human activity classification methods into two large categories according to whether they use data from different modalities or not. Then, each of these categories is further analyzed into sub-categories, which reflect how they model human activities and what type of activities they are interested in. Moreover, we provide a comprehensive analysis of the existing, publicly available human activity classification datasets and examine the requirements for an ideal human activity recognition dataset. Finally, we report the characteristics of future research directions and present some open issues on human activity recognition.

Original languageEnglish (US)
Article number28
JournalFrontiers Robotics AI
Issue numberNOV
StatePublished - 2015


  • Action representation
  • Activity categorization
  • Activity datasets
  • Human activity recognition
  • Review
  • Survey

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence


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