A Self-adaptive CodeBook (SACB) model for real-time background subtraction

Abstract

Effective and efficient background subtraction is important to a number of computer vision tasks. In this paper, we introduce a new background model that integrates several new techniques to address key challenges for background modeling for moving object detection in videos. The novel features of our proposed Self-adaptive CodeBook (SACB) background model are: a more effective color model using YCbCr color space, a statistical parameter estimation method, and a new algorithm for adding new background codewords into the permanent model and deleting noisy codewords from the models. Also, a new block-based approach is introduced to exploit the local spatial information. The proposed model is rigorously tested and has shown significant performance improvements over several previous models.