Shadow elimination for effective moving object detection by Gaussian shadow modeling

This paper presents a novel approach for eliminating unexpected shadows from multiple pedestrians from a static and textured background using Gaussian shadow modeling. First, a set of moving regions are segmented from the static background using a background subtraction technique. The extracted moving region may contain multiple shadows from various pedestrians. In order to remove these unwanted shadows completely, a histogram-based method is proposed for isolating each pedestrian from the extracted moving region. Based on the results, a coarse-to-fine shadow modeling process is then applied for eliminating the unwanted shadow from the detected pedestrian. At the coarse stage, a moment-based method is first used for obtaining the rough shadow boundaries. Then, the rough approximation of the shadow region can be further refined through Gaussian shadow modeling. The chosen shadow model is parameterized with several features including the orientation, mean intensity, and center position of a shadow region. With these features, the chosen model can precisely model different shadows at different conditions and provide good capabilities for completely eliminating the unexpected shadows from the scene background. Due to the simplicity of the proposed method, all the shadows can be eliminated immediately (in less than 0.5 seconds). Experiments demonstrate that approximately 94% of shadows can be successfully eliminated from the scene background.