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

• 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.

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.