Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles

Abstract

Correctly determining the driving area and pedestrians is crucial for intelligent vehicles to reduce fatal road accidents risk. But these are challenging tasks in the computer vision field. Various weather, road conditions, etc., make them difficult. This paper presents a vision-based road segmentation and pedestrian detection system. First, the roads are segmented using a deep learning based consecutive triple filter size (CTFS) approach. Then, pedestrians on the segmented roads are detected using the transfer learning approach. The CTFS approach can create feature maps for small and big features. The proposed system is a reliable, low-cost road segmentation and pedestrian detection system for intelligent vehicles.