Detection and classification of obstacles in night vision traffic scenes based on infrared imagery

Due to the high number of traffic victims at night the detection and classification of obstacles in night scenes is an important goal of vision based driver assistance systems. Especially non-luminescent objects, like humans and animals, are of interest. In our night-vision project, we use a passive far-infrared sensor to achieve this goal. A pixel based statistical classifier, a region-based segmentation algorithm and a polynomial classifier are used to detect and classify objects. The first classifier finds interesting regions with potential objects, the region-based segmentation algorithm is used to resegment those ROIs, and a quadratic polynomial classifier determines the type of the object. The resegmentation module provides an improvement in detection exactness and classification errors.