Moving Cast Shadows Detection Methods for Video Surveillance Applications

Abstract

Moving cast shadows are a major concern in today's performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object ('shape from shadows') as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows). IntroductionVideo Surveillance has been in our society for a long time [6,13]. It began in the twentieth century to assist prison officials in the discovery of escape methods. However, it was not until the late-twentieth century that surveillance expanded to include the security of property and people. Video surveillance is more prevalent in Europe than anywhere in the world. For instance, in the past decade, successive UK governments have installed over 2.4 million surveillance cameras (about one for every 14 people). 1 The average Londoners are estimated to have their picture recorded more than three hundred times a day 2 . Traditionally video surveillance was used to display images on monitors inspected by guards or operators. This fact has allowed the observation of an increase number of places using less people and also to perform patrolling duties from the safety of a control room. However, a single operator can only monitor a limited amount of scenes simultaneously and for a limited amount of time, because the process of manual surveillance is very time-consuming and is a really tedious task.The new breakthroughs in technology have led to a new generation of video surveillance. The current generation of video surveillance systems uses digital computing and communication technologies to improve the design of the original architecture, with the ultimate goal to create an automatic video surveillance system. Recent trends in computer vision has delved into the study of cognitive vision systems, which uses visual information to facilitate a series of tasks on sensing, understanding, reaction and communication. In other words, video surveillance systems aim to automatically identify people, objects or events of ...

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