Horizontal Lines and Haar-like Features for Car Detection Using Support Vector Machine on Traffic Imagery

Abdillah, Aldi Khoirul
Prahara, Adhi

Signal and Image Processing Letters
Vol 3, No 1 (2021)

Traffic monitoring system in Indonesia is not yet efficient. CCTV cameras had been installed to monitor the traffic in strategic locations. However, it is difficult to monitor each traffic point all the time. This problem leads to the development of intelligent traffic monitoring system using computer vision technology. In this research, a car detection method is proposed. Car detection still poses challenges especially when dealing with various situations on the road. The proposed car detection method uses horizontal lines and Haar-like features trained with Support Vector Machine (SVM) to detect cars on traffic imagery. The car detector is trained on frontal-view car dataset. The test result shows 0.2 log average miss rate and 0.9 average precision. From the low miss rate and high precision, the proposed method shows promising solution in detecting cars on traffic imagery.