Human action recognition using ordinal measure of accumulated motion

  • Authors:

  • Wonjun Kim;Jaeho Lee;Minjin Kim;Daeyoung Oh;Changick Kim

  • Affiliations:

  • Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea;Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea;Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea;Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea;Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea

  • Venue:
  • EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
  • Year:
  • 2010

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Abstract

This paper presents a method for recognizing human actions from a single query action video. We propose an action recognition scheme based on the ordinal measure of accumulated motion, which is robust to variations of appearances. To this end, we first define the accumulated motion image (AMI) using image differences. Then the AMI of the query action video is resized to a N×Nsubimage by intensity averaging and a rank matrix is generated by ordering the sample values in the sub-image. By computing the distances from the rank matrix of the query action video to the rank matrices of all local windows in the target video, local windows close to the query action are detected as candidates. To find the best match among the candidates, their energy histograms, which are obtained by projecting AMI values in horizontal and vertical directions, respectively, are compared with those of the query action video. The proposed method does not require any preprocessing task such as learning and segmentation. To justify the efficiency and robustness of our approach, the experiments are conducted on various datasets.