HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway


Firnanda Al Islama Achyunda Putra(1*), Fitri Utaminingrum(2), Wayan Firdaus Mahmudy(3)

(1) Universitas Brawijaya
(2) Faculty of Computer Science, Universitas Brawijaya, Malang
(3) Faculty of Computer Science, Universitas Brawijaya, Malang
(*) Corresponding Author


Autonomous car is a vehicle that can guide itself without human intervention. Various types of rudderless vehicles are being developed. Future systems where computers take over the art of driving. The problem is prior to being attention in an autonomous car for obtaining the high safety. Autonomous car need early warning system to avoid accidents in front of the car, especially the system can be used in the Highway location. In this paper, we propose a vision-based vehicle detection system for Autonomous car. Our detection algorithm consists of three main components: HOG feature extraction, KNN classifier, and vehicle detection. Feature extraction has been used to recognize an object such as cars. In this case, we use HOG feature extraction to detect as a car or non-car. We use the KNN algorithm to classify. KNN Classification in previous studies had quite good results. Car detected by matching about trining data with testing data. Trining data created by extract HOG feature from image 304 x 240 pixels. The system will produce a classification between car or non-car.


Histogram of Oriented Gradient (HOG); K-Nearest Neighbour (KNN); Vehicle Detection

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DOI: https://doi.org/10.22146/ijccs.54050

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