A probabilistic SVM approach for background scene initialization

Horng-Horng Lin, Tyng-Luh Liu, Jen-Hui Chuang

Abstract: Visual tracking systems using background subtraction have been very popular largely due to their efficiency in extracting moving objects. However, such systems often compute the reference background by assuming no moving objects are present during the initialization stage, though the assumption may not be realistic. We propose an automatic way to perform background initialization using a probabilistic SVM (support vector machine). By formulating the problem as an on-line classification one, our approach has the potential to be real-time. SVM classification is carried out for all elements of each image frame by computing the output probabilities. Newly found background elements are evaluated and determined if they should be added to the solution. The process of background initialization continues until there are no more new background elements to be considered. As the features used in an SVM dictate the outcome of classification, we find that optical flow value and inter-frame difference are the two most important ones. Experimental results are included to demonstrate the efficiency of our method.