Real-time robust detection of moving objects in cluttered scenes

Object recognition is a very important task in computer vision and different techniques have been presented to solve it. In this paper a Hough-type low-computational algorithm for detection of objects in cluttered scenes is presented. The approach is based on the detection of the shape of an object, modeled by means of a set of corners. An automatically model learning method is introduced. The method is used in an existing video-surveillance system in order to increase its detection performances. Results show that the proposed approach provides good performances with low processing times.