Self-adapting part-based pedestrian detection using a fish-eye camera

Abstract

Nowadays, fish-eye cameras play an increasingly important role in intelligent vehicles because of its wide field of view. Using fish-eye camera, pedestrians around the vehicles could be monitored expediently, but the problem of pedestrian distortion has always existed. This paper creates a new warping pedestrian benchmark using imaging principle of the fish-eye camera based on ETH pedestrian benchmark. With this practical benchmark, warping pedestrians are trained differently according to the position in fish-eye images. A self-adapting part-based algorithm is proposed to detect pedestrian with different degrees of deformation. Moreover, GPU is used to accelerate the whole algorithm to guarantee the real-time performance. Experiments show that the algorithm has competitive accuracy.