Human Action Recognition Using Ordinal Measure of Accumulated Motion. (12th April 2010)

• Record Type: Journal Article
• Authors: Kim Kim, Wonjun Wonjun; Lee Lee, Jaeho Jaeho; Kim Kim, Minjin Minjin; Oh Oh, Daeyoung Daeyoung; Kim Kim, Changick Changick
• Other Names: Hwang Hwang Jenq-Neng Jenq-Neng Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-04-12
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/219190
• Languages: English
• ISSNs: 1687-6172

Abstract

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 × N subimage 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.