A shadow elimination approach in video-surveillance context
Moving objects tracking is an important problem in many applications such as video-surveillance. Monitoring systems can be improved using vision-based techniques able to extract and classify objects in the scene. However, problems arise due to unexpected shadows because shadow detection is critical for accurate objects detection in video stream, since shadow points are often misclassified as object points causing errors in localization, segmentation, measurements, tracking and classification of moving objects. The paper presents a new approach for removing shadows from moving objects, starting from a frame-difference method using a grey-level textured adaptive background. The shadow detection scheme uses photometric properties and the notion of shadow as semi-transparent region which retains a reduced-contrast representation of the underlying surface pattern and texture. We analyze the problem of representing texture information in terms of redundant systems of functions for texture identification. The method for discriminating shadows from moving objects is based on a Pursuit scheme using an over-complete dictionary. The basic idea is to use the simple but powerful Matching Pursuit algorithm (MP) for representing texture as linear combination of elements of a big set of functions. Particularly, MP selects the best little set of atoms of 2D Gabor dictionary for features selection representative of properties of the texture in the image. Experimental results validate the algorithms performance.
video-surveillance gabor dictionary frame theory shadow removal texture analysis