Extraction of Foreground Entities Through an Adaptive Background Framework
This paper proposes a novel method for detection and segmentation of foreground objects from a video, which contains both stationary and moving background objects and undergoes both gradual and sudden "once-off" changes through background modeling. A Bayes decision rule for classification of background and foreground from selected feature vectors is formulated. Under this rule, different types of background objects will be classified from foreground objects by choosing a proper feature vector. The color feature describes the stationary background object, and the color co-occurrence feature represents the moving background object. Foreground objects are extracted by fusing the classification results from both stationary and moving pixels. Learning strategies for the gradual and sudden "once-off" background changes are proposed to adapt to various changes in background through the video. The convergence of the learning process is proved and a formula to select a proper learning rate is also derived. Experiments have shown promising results in extracting foreground objects from many complex backgrounds including offices, public buildings, subway stations, campuses, parking lots, airports, and sidewalks.
Keywords: Background Maintenance, Background Modeling, Background Subtraction, Bayes Decision Theory, Complex Background, Feature Extraction, Motion Analysis, Object Detection, Principal Features, Video Surveillance
Ruba Soundar Kathavarayan
Lecturer, Computer Science and Engineering Department, Mepco Schlenk Engineering College
ME Student, Computer Science and Engineering Department, Mepco Schlenk Engineering College