Accurate video object segmentation through change detection

We propose an algorithm for the accurate extraction of video objects from color sequences. The semantics defining the video objects is motion, and the extraction algorithm is based on change detection. The color difference between frames is modeled so as to separate the contributions caused by sensor noise and illumination variations from those caused by meaningful objects. Sensor noise is eliminated by using a probability-based classification, and local illumination variations are removed using a knowledge-based approach that is formulated as a hypothesize-and-test scheme. Experimental results show that the proposed method provides accurate contours of multiple deformable objects, thus providing a reliable input to object-based applications such as those supported by the MPEG-4 and MPEG-7 standards.