Abstract
Aiming at the problems that the classical Gaussian mixture model is unable to detect the complete moving object, and is sensitive to the light mutation scenes and so on, an improved algorithm is proposed for moving object detection based on Gaussian mixture model and three-frame difference method. In the process of extracting the moving region, the improved three-frame difference method uses the dynamic segmentation threshold and edge detection technology, and it is first used to solve the problems such as the illumination mutation and the discontinuity of the target edge. Then, a new adaptive selection strategy of the number of Gaussian distributions is introduced to reduce the processing time and improve accuracy of detection. Finally, HSV color space is used to remove shadow regions, and the whole moving object is detected. Experimental results show that the proposed algorithm can detect moving objects in various situations effectively.
Highlights
The performance of moving object detection algorithm is very crucial in a sound surveillance system for reliable tracking and behavior recognition
Based on Gaussian mixture model and three-frame differencing method, we propose a moving object detection algorithm in this paper
This paper proposes a moving object detection algorithm which based on Gaussian mixture model and threeframe difference method
Summary
The performance of moving object detection algorithm is very crucial in a sound surveillance system for reliable tracking and behavior recognition. Stauffer et al proposed Gaussian mixture model (GMM) [5] used for background modeling which had been found to cope reliably with slow illumination changes, repetitive motions from clutter, and long-term scene changes. This method suffers from many problems in the process of object detection. Zivkovic proposed an adaptive GMM algorithm with the maximum likelihood estimation [7], which adaptively chose the appropriate number of Gaussian distributions to model each pixel on-line This method reduces the processing time and improves the accuracy of segmentation slightly. The experiments show that our algorithm has improved in the aspects of accuracy, adaptability and real-time performance
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