Pedestrian detection in poor weather conditions using moving camera

Abstract

Many challenges are present in the pedestrian detection field which makes it a trending topic. Detecting pedestrian is an extremely difficult task under bad weather conditions. In order to improve and facilitate the detection task, it is required to use infra-red images. For the advanced driver-assistance systems (ADAS), more specifically those of the pedestrian detection, the camera is mounted on a moving vehicle resulting egomotion in the background. Thus another challenging problem is added. It is then required to compensate the background egomotion to obtain a background static scene. In this paper, we introduce an advanced approach for the pedestrian detection under poor weather conditions using a moving camera. First, using the interest point detector Speeded Up Robust Features (SURF), ego-motions in the background are adjusted. After that, the foreground is detected by subtracting frames. Then, a segmentation step is required to divide the images into multiple moving objects. Finally, a recognition process is applied in order to classify the moving objects into both categories: pedestrian and undefined patterns. The proposed approach was evaluated on the CVC14 dataset. Experimental results illustrate the good performance of the approach.