Accurate video object segmentation through change detection

We propose an algorithm for the accurate extraction of video objects from color sequences. The semantics defin-ing the video objects is motion, and the extraction algo-rithm is based on change detection. The color difference between frames is modeled so as to separate the contri-butions 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 stan-dards