Deteksi Kendaraan Roda Empat untuk Mendukung Keamanan Berkendara Menggunakan Histogram Of Oriented Gradients dan Support Vector Machine Berbasis Raspberry Pi - Intan Fatmawati; Fitri Utaminingrum; Wijaya Kurniawan (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Driving safety is the most important thing in driving on the highway to avoid accidents. Accidents occur due to several factors including lack of concentration while driving, drowsiness, and so forth. One effort to avoid or reduce the risk of accidents when driving is to maintain the distance of the vehicle with the vehicle in front of it. In this study the theme of image processing is used to achieve the goal of maintaining distance between vehicle by utilizing the Histogram of Oriented Gradients (HOG) method as a way of extracting vehicle features which in this case are cars, then classified by the Vector Support Machine (SVM) method to distinguish between car classes and not cars, the two methods are implemented uses a Raspberry Pi camera mounted on the dashboard to detect the vehicle in front of it. When the distance of the vehicle with the vehicle in front is less than or equal to 15 meters (â‰¤15m), the buzzer will sound as a sign that the vehicle is too close. The accuracy of the system in detecting cars using Support Vector Machine (SVM) that based on the Histogram of Oriented Gradients (HOG) feature by testing at a distance of 10 m, 15 m, 20 m, dan 30 m is 81.3% and the testing accuracy of Hardware and Software integration is 87.5%.