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Moving Object Detection using Clausius Entropy and Adaptive Gaussian Mixture Model  

Park, Jong-Hyun (School of Electronic and Computer Engineering, Chonnam National University)
Lee, Gee-Sang (School of Electronic and Computer Engineering, Chonnam National University)
Toan, Nguyen Dinh (School of Electronic and Computer Engineering, Chonnam National University)
Cho, Wan-Hyun (Dept. of Statistical, Chonnam National University)
Park, Soon-Young (Department of Electronics Engineering, Mokpo National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.47, no.1, 2010 , pp. 22-29 More about this Journal
Abstract
A real-time detection and tracking of moving objects in video sequences is very important for smart surveillance systems. In this paper, we propose a novel algorithm for the detection of moving objects that is the entropy-based adaptive Gaussian mixture model (AGMM). First, the increment of entropy generally means the increment of complexity, and objects in unstable conditions cause higher entropy variations. Hence, if we apply these properties to the motion segmentation, pixels with large changes in entropy in moments have a higher chance in belonging to moving objects. Therefore, we apply the Clausius entropy theory to convert the pixel value in an image domain into the amount of energy change in an entropy domain. Second, we use an adaptive background subtraction method to detect moving objects. This models entropy variations from backgrounds as a mixture of Gaussians. Experiment results demonstrate that our method can detect motion object effectively and reliably.
Keywords
object detection; Clausius entropy; adaptive Gaussian mixture model; surveillance system;
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