Aiming at reducing the high computation cost of the classic Gaussian mixture model (GMM)

we propose a GMM with spatial constraint (SCGMM). The main reason for the high computation cost of the GMM is that all pixel models are computed at every frame

and a large part of the computation is useless for GMM. Thus

the SCGMM focuses on reducing the number of models involved in the computation of the GMM. For the three parts of the GMM

three methods are utilized to reduce the computation cost of the GMM. In the initial part of the GMM

a method of fast initialization is utilized to shorten the process of initial modeling. The initialization of the GMM requires a large amount of statistical information from all the pixel points. Each pixel should be involved in all operations and the computation cost for one frame used in the initial part cannot be obviously reduced. For this reason

a simple adaptive learning rate is applied to reduce the number of frames required in the initial part of the GMM. In the moving object detection part of the GMM

a double background model is adopted. The detection results for moving objects of the first adaptive background model are used as the spatial constraint condition of the GMM to reduce the redundant computation of the GMM at the region without moving objects. The moving object detection method of the GMM is also used at the region that may contain moving objects to maintain the accuracy of the GMM. Therefore

the advantages of the SCGMM in the moving object detection part is that the SCGMM reduces the number of pixels involved in the computation of the GMM and maintains the accuracy of the GMM. In the parameter update part

multi-strategy adaptive model updating is adopted. The final result of the moving object detection part is used as the spatial constraint condition to reduce the quantity of pixels involved in parameter update. Adaptive learning rate and periodic global update are applied to improve the accuracy of moving object detection. By using the aforementioned methods

the performance of GMM is evidently optimized. Experimental results show that SCGMM has better performance and accuracy than GMM

CodeBook

GMG

and MODGMM(mean of deviation GMM). The processing speed is increased by more than three times. Notably

the processing speed of SCGMM is increased by more than six times compared with that of GMM

and the percentage of pixels involved in the complex computation process is less than 20%. Compared with GMM

SCGMM has better performance at real-time processing and better accuracy in a fixed camera scene.