بازشناسی انسان در سیستم‌های نظارت ویدئویی

People re-identification is one of the most important and fundamental processes in video surveillance systems. The accuracy and efficiency of this task influence the effectiveness of the subsequent processes. Event detection and behavior analysis are instances of such subsequent processes that are classified in semantic levels. In people re-identification, having an image or video of an individual in a specific camera, we want to infer whether the person has been seen previously in the other cameras. Various changes in people’s appearance, recorded by different cameras, can lead to many difficulties in people re-identification. These changes may occur due to hardware limitations such as low-resolution, different color responses of cameras, lighting changes in different camera locations and person’s angle relative to the camera. In this paper we will present a review on the existing approaches in people re-identification, then, a new classification of these works based on their functionalities is provided. Our studies shows that the problem of changing the camera angles has not been seriously considered in the existing approaches and future research could provide models that able to resolve these problems.