Hierarchical Data Structure for Real-Time Background Subtraction

This paper seeks to increase the efficiency of background subtraction algorithms for motion detection. Our method uses a quadtree-base hierarchical framework that samples a small portion of the pixels in each image and yet produces motion detection results that are very similar compared to the conventional methods that raster scan entire images. The hierarchical data structure presented in this paper can be used with any background subtraction algorithm that employs background modeling and motion detection on a per-pixel basis. We have tested our method using two common background subtraction algorithms: running average and mixture of Gaussian. Our experimental results show that the application of the hierarchical data structure significantly increases the processing speed for accurate motion detection. For example, the mixture of Gaussian method with our hierarchical data structure is able to process 1600 by 1200 images at 11~12 frames per second compared to 2~3 frames per second without using the hierarchical data structure.