Open Access

Combination of Accumulated Motion and Color Segmentation for Human Activity Analysis

  • Alexia Briassouli1Email author,
  • Vasileios Mezaris1 and
  • Ioannis Kompatsiaris1
EURASIP Journal on Image and Video Processing20072008:735141

https://doi.org/10.1155/2008/735141

Received: 1 February 2007

Accepted: 12 December 2007

Published: 12 December 2007

Abstract

The automated analysis of activity in digital multimedia, and especially video, is gaining more and more importance due to the evolution of higher-level video processing systems and the development of relevant applications such as surveillance and sports. This paper presents a novel algorithm for the recognition and classification of human activities, which employs motion and color characteristics in a complementary manner, so as to extract the most information from both sources, and overcome their individual limitations. The proposed method accumulates the flow estimates in a video, and extracts "regions of activity" by processing their higher-order statistics. The shape of these activity areas can be used for the classification of the human activities and events taking place in a video and the subsequent extraction of higher-level semantics. Color segmentation of the active and static areas of each video frame is performed to complement this information. The color layers in the activity and background areas are compared using the earth mover's distance, in order to achieve accurate object segmentation. Thus, unlike much existing work on human activity analysis, the proposed approach is based on general color and motion processing methods, and not on specific models of the human body and its kinematics. The combined use of color and motion information increases the method robustness to illumination variations and measurement noise. Consequently, the proposed approach can lead to higher-level information about human activities, but its applicability is not limited to specific human actions. We present experiments with various real video sequences, from sports and surveillance domains, to demonstrate the effectiveness of our approach.

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Authors’ Affiliations

(1)
Centre for Research and Technology Hellas, Informatics and Telematics Institute

Copyright

© Alexia Briassouli et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.