Moving object detection based on improved single Gaussian background model

作者：Chen Yin^*; Ren Kan; Gu Guohua; Qian Weixian; Xu Fuyuan

来源：Chinese Journal of Lasers, 2014, 41(11): 1109002.

DOI：10.3788/CJL201441.1109002

• Background differences
• Background model
• Mean shift
• Moving objects
• Sample means

Aiming at the non-adaptive problem and incomplete detection of single Gaussian background model (SGM), an improved single Gaussian background model method for moving object detecton is proposed. This method combines SGM and the mean shift algorithm to detect moving objects. The initial background model is decided by using N frames of images, and the moving objects are detected, the pixel which belong to background points are updated according to the single Gaussian model algorithm, and the pixel which do not belong to the background points in the updated background model are corrected using the mean shift algorithm, the background model corrected by the mean shift algorithm is used as the final background model. The moving objects are detected using the background difference method. The experiments show that the improved method can overcome the non-adaptive shortcoming and have high detectivity.

• 出版日期2014
• 单位南京理工大学