Single camera vehicle detection using edges and bag-of-features

Abstract

Vehicle detection is becoming a necessary part of Automatic Cruise Control (ACC) and Advanced Driver Assistance Systems (ADAS). Our main focus in this paper is on improving the performance of single camera based vehicle detection systems. Edges are one of the main characteristics of an object, which carries most of the information about an object in an image. In this paper, it was observed that horizontal edges are strong feature for vehicle detection. Therefore, we generated initial candidate using Horizontal Edge Filtering (HEF) on canny edge map. These initial candidates are further verified using Bag-of-Features (BoF) with K nearest neighbor algorithm. A threshold is used on differences of histograms of training and test images for matching the vehicles. In this paper, the combination of edges (initial candidate) and bag-of-features (final verification) has improved detection rate significantly as compared to other well known methods. Our method has 96% detection rate on roads inside a city and 98% detection on highways. © 2012 Springer Science+Business Media B.V.