Realtime Background Subtraction from Dynamic Scenes

This paper examines the problem of moving object detection. More precisely, it addresses the difficult scenarios where background scene textures in the video might change over time. In this paper, we formulate the problem mathematically as minimizing a constrained risk functional motivated from the large margin principle. It is a generalization of the one class support vector machines (1-SVMs) [17] to accommodate spatial interactions, which is further incorporated into an online learning framework to track temporal changes. As a result it yields a closed-form update formula, a central component of the proposed algorithm to enable prompt adaptation to spatio-temporal changes. We also analyze the mistake bound and discuss issues such as dealing with non-stationary distributions, making use of kernels and efficient inference by a variant of dynamic programming. By exploiting the inherently concurrent structure, the proposed approach is designed to work with the highly parallel graphics processors (GPUs) to facilitate realtime analysis. Our empirical study demonstrates that the proposed approach works in realtime (over 80 frames per second) and at the same time performs competitively against state-of-the-art offline and quasi-realtime methods.