Segmentation of Motion Objects from Surveillance Video Sequences Using Temporal Differencing Combined with Multiple Correlation

Identifying moving objects from a video sequence is a fundamental and critical task in many computer vision applications. We develop an efficient adaptive segmentation algorithm for color video surveillance sequence in real time with non-stationary background; background is modeled using multiple correlation coefficient using pixel-level based approach. At runtime, segmentation is performed by checking color intensity values at corresponding pixels P(x,y) in three frames using temporal differencing (frame gap three). The segmentation starts from a seed in the form of 3times3 image blocks to avoid the noise. Usually, temporal differencing generates holes in motion objects. After subtraction, holes are filled using image fusion, which uses spatial clustering as criteria to link motion objects. The emphasis of this approach is on the robust detection of moving objects even under noise or environmental changes (indoor as well as outdoor).