Efficient region-based motion segmentation for a video monitoring system

  • Authors:

  • Jong Bae Kim;Hang Joon Kim

  • Affiliations:

  • Artificial Intelligence (AI) Laboratory, Department of Computer Engineering, Kyungpook National University, 1370 Sangyuk-dong, Puk-gu, Daegu 702-701, South Korea;Artificial Intelligence (AI) Laboratory, Department of Computer Engineering, Kyungpook National University, 1370 Sangyuk-dong, Puk-gu, Daegu 702-701, South Korea

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2003

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Abstract

This paper presents an efficient region-based motion segmentation method for segmentation of moving objects in a traffic scene with a focus on a video monitoring system (VMS). The presented method consists of two phases: first, in the motion detection phase, the positions of moving objects in a scene are determined using an adaptive thresholding method. To detect varying regions by moving objects, instead of determining the threshold value manually, we use an adaptive thresholding method to automatically choose the threshold value. Second, in the motion segmentation phase, pixels that have similar intensity and motion information are segmented using a weighted k-means clustering algorithm to the binary region of the motion mask obtained in the motion detection. In this way, we need not process a whole image so computation time is reduced. Experimental results demonstrate robustness not only in the variation of luminance conditions and changes in environmental conditions, but also for occlusions among multiple moving objects.