IR-videostream rendering based on high-level object information

IR-sensors are mainly utilized in video surveillance systems in order to provide vision during nighttime and in di use lighting conditions. The dynamic range of IR-sensors usually exceeds that of conventional display devices. Hence, range compression associated with loss of information is always required. Range compression methods can be divided into global methods, which are based on the intensity distribution, and local methods focused on smaller regions of interest. In contrast to local methods, global methods are computationally efficient. Nevertheless, global methods have the drawback that fine details can be suppressed by intensity changes at image locations which are unrelated to the object of interest. In order to overcome these restrictions, we propose a method to render IR images based on high level object information. The overall processing pipeline consists of a contrast enhancement method, followed by object detection, and a range compression method that takes the location of objects into account. Here we use pedestrians as an exemplary object category. The output of the detector is a rectangular bounding box, centered at the person location. Restricting range compression to a person location, allows to display details on the person surface that most probably would remain undetected using global range compression methods. The proposed combination of rendering with high level information is intended to be integrated in a surveillance system to assist human operators. Towards this end, this paper provides some insights into the design of visualization tools