Adaptive and Efficient Background Subtraction Using Multi-Models
自适应多模快速背景差算法

This paper presents an efficient background subtraction algorithm using multiple scene models to cope with variations of noises in a background.A mechanism has been developed to add and delete scene models so that the distribution of the models is adaptive to the background characteristics.The calculation for the model parameters has been optimized so as to avoid time-consuming floating point calculation.We introduced the living time and recurrent frequency to the models so that the algorithm can suppress high frequency background noises effectively by controlling the model recurrent frequency.Experiments using video data have been conducted to compare the performance of our algorithm with that of the mixture Gaussian model algorithm.The experimental results demonstrated that our algorithm can extract the foreground contour more precisely,efficiently and with less memory,while maintaining the advantages of the mixture Gaussian model algorithm.It was also found that high frequency noises that cannot be rejected by the mixture Gaussian model can be suppressed.