Evaluation of background subtraction algorithms for video surveillance

This paper presents a comparative study of several state of the art background subtraction (BS) algorithms. The goal is to provide brief solid overview of the strengths and weaknesses of the most widely applied BS methods. Approaches ranging from simple background subtraction with global thresholding to more sophisticated statistical methods have been implemented and tested with ground truth. The interframe difference, approximate median filtering and Gaussian mixture models (GMM) methods are compared relative to their robustness, computational time, and memory requirement. The performance of the algorithms is tested in public datasets. Interframe difference and approximate median filtering are pretty fast, almost five times faster than GMM. Moreover, GMM occupies five times more memory than simpler methods. However, experimental results of GMM are more accurate than simple methods.