An Enhanced Background Estimation Algorithm for Vehicle Detection in Urban Traffic Scenes

Abstract

This paper proposes a new background subtraction algorithm based on the sigma-delta filter, which is intended to be used in urban traffic scenes. The original sigma-delta algorithm is a very interesting alternative due to its high computational efficiency. However, the background model quickly degrades in complex urban scenes because it is easily “contaminated” by slow-moving or temporarily stopped vehicles. Then, subsequent foreground validation steps are needed to refine the foreground detection mask. Instead of requiring any subsequent processing steps or resorting to algorithms with higher computational cost, the proposed algorithm tries to achieve a more stable background model by introducing a confidence measurement for each pixel. This confidence measurement assists in a selective background-model updating mechanism at the pixel level. Experimental comparative tests and a quantitative performance evaluation over typical urban traffic sequences corroborate the benefits of the proposed algorithm.