Vehicle Detection with HOG and Linear SVM

Abstract

In this paper, we present a vehicle detection system by employing Histogram of Oriented Gradients (HOG) for feature extraction and linear SVM for classification. We study the influence of the colour space on the performance of the detector, concluding that decorrelated and perceptual colour spaces give the best results. An in-depth analysis is carried out on the effects of the HOG and SVM parameters, the threshold for the distance between features and the SVM classifying plane, and the non-maximum suppression (NMS) threshold on the performance of the detector, and we propose values that illustrate good performance for vehicle detection on images. We also discuss the issues of the approach and the reasons for its mediocre performance on videos. Finally, we address these issues by presenting ideas that can be considered for improving the system.

Keywords

• computer vision
• machine learning
• HOG
• SVM
• color space
• vehicle detection
• autonomous vehicles

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