An Improved Gaussian Mixture Model Method for Moving Object Detection

Weiwei Dong, Yujian Wang, Wenpeng Jing, Taoxin Peng


Aiming at the shortcomings of Gaussian mixture model background method, a moving object detection method mixed with adaptive iterative block and interval frame difference method in the Gaussian mixture model is proposed. In this method, the video sequences are divided into different size pieces in order to reduce the amount of calculation of the algorithm. It not only effectively solves the problem that the traditional Gaussian mixture model algorithm cannot detect large and slow moving object accurately, but also solves empty and no connection problems due to the introduction of block thought. The experimental results show that the improved algorithm has faster processing speed, better effect and better environment adaptability compared with the background of the Gaussian mixture model method. And it can detect moving object more accurately and completely.



Moving object detection; Gaussian mixture model; block thought; interval frame difference method

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