Improved Gaussian Mixtures for Robust Object Detection by Adaptive Multi-Background Generation

Adaptive Gaussian mixtures are widely used to model the dynamic background for real-time object detection. Recently the convergence speed of this approach is improved and a relatively robust statistical framework is proposed by Lee (PAMI, 2005). However object quality still remains unacceptable due to poor Gaussian mixture quality, susceptibility to background/foreground data proportion, and inability to handle intrinsic background motion. This paper proposes an effective technique to eliminate these drawbacks by modifying the new model induction logic and using intensity difference thresholding to detect objects from one or more believe-to-be back-grounds. Experimental results on two benchmark datasets confirm that the object quality of the proposed technique is superior to that of Lee's technique at any model learning rate.