Pedestrian Detection in Automotive Safety: Understanding State-of-the-Art

Abstract

Pedestrian detection is one of the important computer vision problems in automotive safety and driver assistance domain. It is a major component of the advanced driver assistance system (ADAS) which help the driver to drive safely. Recent literature shows a number of research activities addressing object detection/tracking in general and pedestrian detection in particular. The solutions proposed by different researchers vary in detection methods, detection scenario, feature descriptors, classification schemes, detection performance, as well as computational complexity. However, the average detection accuracy is not much promising even after many years of research. The fail-safe and real-time human detection from real life road scenes, even in standard resolution, is far from reality. Safety critical systems in the automotive industry have to follow well established stringent safety standards like ISO26262. Since the pedestrian detection system deals with human safety, it also has to follow these standards before integrating to the vehicle electronics. This paper is a study of different techniques used in pedestrian detection specific to the automotive application, along with a description of generic pedestrian detection solution architecture.