VEHICLE DETECTION FOR NIGHTTIME USING MONOCULAR IR CAMERA WITH DISCRIMINATELY TRAINED MIXTURE OF DEFORMABLE PART MODELS

Abstract

Vehicle detection at night time is a challenging problem due to low visibility and light distortion caused by motion and illumination in urban environments. This paper presents a method based on the deformable object model for detecting and classifying vehicles using monocular infra-red camera. In proposed method, features of vehicles are learned as a deformable object model through the combination of a latent support vector machine (LSVM) and histograms of oriented gradients (HOG). The proposed detection algorithm is flexible enough in detecting various types and orientations of vehicles as it can effectively integrate both global and local information of vehicle textures and shapes. Experimental results prove the effectiveness of the algorithm for detecting close and medium range vehicles in urban scenes at night time.