A Robust GA/KNN Based Hypothesis Verification System for Vehicle Detection

Abstract

Vehicle detection is an important issue in driver assistance systems and self-guided vehicles that includes two stages of hypothesis generation and verification. In the first stage, potential vehicles are hypothesized and in the second stage, all hypothesis are verified. The focus of this work is on the second stage. We extract Pyramid Histograms of Oriented Gradients (PHOG) features from a traffic image as candidates of feature vectors to detect vehicles. Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are applied to these PHOG feature vectors as dimension reduction and feature selection tools parallelly. After feature fusion, we use Genetic Algorithm (GA) and cosine similarity-based K Nearest Neighbor (KNN) classification to improve the performance and generalization of the features. Our tests show good classification accuracy of more than 97% correct classification on realistic on-road vehicle images.