Vehicle Detection with HOG and Linear SVM
Journal Title: Journal of Emerging Computer Technologies - Year 2021, Vol 1, Issue 1
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.
Authors and Affiliations
Nikola TOMiKJ, Andrea KULAKOV
Keywords
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- EP ID EP706075
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Vehicle Detection with HOG and Linear SVM
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 performa...