Segmentation of Foreground in Image Sequence with Foveated Vision Concept

The human visual system has no difficulty to detect moving object. To design an automated method for detecting foreground in videos captured in a variety and complicated scenes is a challenge. The topic has attracted much research due to its wide range of video-based applications. We propose a foveated model that mimics the human visual system for the detection of foreground in image sequence. It is a two-step framework simulating the awareness of motion followed by the extraction of detailed information. In the first step, region proposals are extracted based on similarity of intensity and motion features with respect to the pre-generated archetype. Through integration of the similarity measures, each image frame is segregated into background and foreground points. Large foreground regions are preserved as region proposals (RPs). In the second step, analysis is performed on each RP in order to obtain the accurate shape of moving object. Photometric and textural features are extracted and matched with another archetype. We propose a probabilistic refinement scheme. If the RP contains a point initially labeled as background, it can be converted to a foreground point if its features are more similar to neighboring foreground points than neighboring background points. Both archetypes are updated immediately based on the segregation result. We compare our method with some well-known and recently proposed algorithms using various video datasets.