High Speed Multi-Layer Background Subtraction

Moving object detection is an important and fundamental step for intelligent video surveillance systems, since it provides a focus of attention for post-processing. In this study, a multi-layer codebook-based background subtraction model is proposed to cope with high resolution video sequences for the detection of moving objects. Combining the multi-layer block-based strategy and the feature extraction of blocks, the proposed method can remove most of the background, including non-stationary (dynamic) background, and significantly increase the processing efficiency. Moreover, pixel-based classification is adopted for refining the results of block-based background subtraction, which can further classify pixels as foreground, shadows, and highlights. The experimental results demonstrate that the proposed multi-layer codebook-based background subtraction method (for standard definition video sequences) can provide a high precision and efficient processing speed for moving objects detection. Moreover, extensive experiments have been conducted to compare with various former schemes, and the superiority of the proposed method shows that it can be a very effective candidate for real-time intelligent video surveillance applications.