GA Based PHOG-PCA Feature Weighting for On-Road Vehicle Detection
Keywords:
Feature weighting, GA, linear SVM, PCA, PHOG, 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 to
classify vehicle candidate images into vehicle and non-vehicle
classes. 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)
is applied to these PHOG feature vectors as a dimension
reduction tool to obtain the PHOG- PCA vectors. Then we
employ real coded chromosome Genetic Algorithm (GA) and
linear Support Vector Machine (SVM) to classify the
PHOG-PCA features as well as to improve their performance
and generalization. Our tests show good classification accuracy
of more than 96% correct classification on realistic on-road
vehicle images.
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