scholarly journals An Improved Moving Object Detection Algorithm Based on Gaussian Mixture Models

2016 ◽  
Vol 06 (07) ◽  
pp. 449-456 ◽  
Author(s):  
Xuegang Hu ◽  
Jiamin Zheng
2013 ◽  
Vol 721 ◽  
pp. 587-590
Author(s):  
Zhao Xia Fu ◽  
Li Ming Wang

The moving object detection of the video image is the basis of sequence image analysis, and it is the research hot issue of today’s foreign and domestic scholars. For detecting the moving object from the scene image in time, a detection algorithm of video moving object based on Gaussian mixture models is proposed in the paper. The pixel values are seen as the combination of the foreground Gaussian distribution and the background Gaussian distribution, and the background estimation and the adaptive background update will be put up. The statistical number of the foreground pixel of the current frame determines whether the light has a larger change, and it combines with the frame-difference method to detect moving object. The experimental results show that the algorithm can quickly and accurately establish the background model and accurately segment the foreground object.


2014 ◽  
Vol 962-965 ◽  
pp. 2848-2851
Author(s):  
Jun Song

Moving object detection and tracking based on the video stream are both challenging and very broad application prospects of research topics. This paper presents a object detection algorithm based on statistical classification of video stream pixels that can solve moving object detection in the background illumination mutations. The algorithm determines the number of points of light mutations by the statistical number of foreground pixels of the current frame. In the light mutations object detection uses relatively simple frame-difference method, otherwise it adopts the improved Gaussian mixture model method to model. Experimental results show that the algorithm can complete detecting in the light mutation quickly and accurately and has strong robustness.


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