Moving Foreground Detection Based on Modified Codebook

This paper presents a modified codebook model for real-time moving foreground detection. The proposed method is an effective combination of background modeling and motion detection. Without a long training sequence, the background model can be represented in a compressed form, a series of codebooks, which means sample background values for each pixel are quantized into codebooks that can used in detection process. In this way, we can capture the structural variation of background in different conditions such as periodic-like motion, hostile environment or change of scene caused by moving object over a long period of time under limited memory. Compared with the original codebook model, this proposed method is more efficient in computation and takes up less memory. Experimental results show that the proposed algorithm is effective, quick for motion detection, and can meet the demands of real-time applications.