Comparative study of computational time that HOG-based features used for vehicle detection

Abstract

HOG produces a number of redundant and long features so that they affect to the detection rate and computational time. This paper studied the processes that HOG-based features were generated, selected, and used in vehicle detection and find one that takes the shortest time. There were five combinations of feature extractors and classifiers. Time spent in HV step, accuracy of detection and the false positive rate are considered together for making decision of which combination is the best. The experiments were conducted on GIT dataset. The experimental results showed that process which VHOG preceded ELM provided a little less accurate than HOG preceded SVM did. However, it did not only take shortest time in HV step but also provided the lowest false positive rate. Therefore, VHOG preceded ELM should be selected as a method for vehicle detection.