Moving Object Segmentation by Background Subtraction and Temporal Analysis

In this paper we address the problem of moving object segmentation using background subtraction. Solving this problem is very important for many applications: visual surveillance of both in outdoor and indoor environments, traffic control, behaviour detection during sport activities, and so on. All these applications require as a first step the detection of moving objects in the observed scene before applying any further technique for object recognition and activity identification. We propose a reliable foreground segmentation algorithm that combines temporal image analysis with a reference background image. We are especially careful of the core problem arising in the analysis of outdoor daylight scenes: continuous variations of lighting conditions that cause unexpected changes in intensities on the background reference image. In this paper a new approach for background adaptation to changes in illumination is presented. All the pixels in the image, even those covered by foreground objects, are continuously updated in the background model. The experimental results demonstrate the effectiveness of the proposed algorithm when applied in different outdoor and indoor environments.