Liyuan Li Institute for Infocomm Research (Singapore) Irene Yu-Hua Gu Chalmers Univ. of Technology (Sweden) Maylor K. H. Leung Nanyang Technological Univ. (Singapore) Tian Qi Institute for Infocomm Research (Singapore)
Background subtraction is an important issue for achieving effective foreground object detection in video surveillance. Background subtraction requires the timely updating of a background model to gradual illumination changes as well as the significant changes in the background. It is also essential that foreground objects have little impact on the updating of the background. Based on our change-type categories, we propose an adaptive background subtraction method where a two-strategy-based background maintenance is introduced to adapt to different types of changes by using feedback from change segmentation and region classification. The work mainly contributes to the following issues: 1. propose a change segmentation method that detects change regions as well as provides spatiotemporal information about the changes by using fuzzy techniques; 2. propose a fuzzy reasoning method to classify background and foreground changes at the object level; and 3. propose a new method for adaptive background maintenance based on the feedback from pixel-level to object-level processing that is able to avoid tradeoff in the updating rate. Experiments on indoor and outdoor video scenes are conducted and results show that the proposed method adapts well to various background changes without absorbing foreground objects. Comparisons with an existing method using a constant learning rate show an improved performance.