A self-adaptive subtraction algorithm for dynamic background video

This paper presents an effective background modeling method that incorporates adaptive mechanism for the dynamic background. Each pixel in the background model is defined by a history of the N most recent image values at each pixel. It then compares the model with the current pixel value to determine whether or not the pixel belongs to the background using the decision threshold. We design the Time-spatial dynamic feature (TSD feature) innovatively to describe the dynamic background. According to the TSD feature, the decision threshes can be adjusted adaptively with feedback loops that overcome global threshold influence for dynamic background. Updating the background model is essential in order to account for changes in the background, such as moving background objects and lighting changes. The update rate in the background model also can be adjusted adaptively with the background changes based on the TSD feature. The experimental results demonstrate that the proposed algorithm outperforms several state-of-the-art methods on dynamic background video sequences.