Semantic image segmentation for pedestrian detection

Abstract

A typical traffic monitoring system for pedestrian detection uses a stationary camera. In Advanced Driving Assistance Systems (ADAS), the camera is mounted in front of the vehicle's window so that the camera and the object move in any arbitrary direction. Semantic image segmentation is widely used for road scene interpretation. In this paper, a method for semantic image segmentation using a convolution neural network is proposed. After a candidate region is segmented we perform pedestrian detection based on shape and size features of the candidate region. The experiments show that the proposed approach can accurately detect pedestrians in real-time (40fps).