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

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. This paper presents a Self-adaptive CodeBook background model for moving object segmentation in a video.Several new techniques are introduced to enhance the performance of standard CodeBook model.The proposed model gives better processing speed than the standard CodeBook model.New color model and the automatic parameter estimation mechanism help to achieve better accuracy than the standard CodeBook model.The proposed model gives a real-time performance and a good balance between segmentation accuracy and processing efficiency.