Human behavior analysis based on a new motion descriptor

  • Authors:

  • Kaiqi Huang;Shiquan Wang;Tieniu Tan;Stephen J. Maybank

  • Affiliations:

  • National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing, China;Department of Computer Science and Information Systems, Birkbeck College of London University, London, UK

  • Venue:
  • IEEE Transactions on Circuits and Systems for Video Technology
  • Year:
  • 2009

Quantified Score

Hi-index 0.02

Visualization

Abstract

Human behavior analysis is an important area of research in computer vision and is also driven by a wide spectrum of applications, such as smart video surveillance and human-computer interface. In this paper, we present a novel approach for human behavior analysis. Two research challenges, motion representation and behavior recognition, are addressed. A novel motion descriptor, which is an improved feature based on optical flow, is proposed for motion representation. Optical flow is improved with a motion filter, and feature fusion with the shape and trajectory information. To recognize the behavior, the support vector machine is employed to train the classifier where the concatenation of histograms is formed as the input features. Experimental results on the Weizmann behavior database and the Institute of Automation, Chinese Academy of Science real-world multiview behavior database demonstrate the robustness and effectiveness of our method.