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Imam Teguh Mulyawan
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Motorcycles detection using Haar-like features and Support Vector Machine on CCTV camera image Imam Teguh Mulyawan; Adhi Prahara
Jurnal Informatika Vol 13, No 2 (2019): July 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (724.417 KB) | DOI: 10.26555/jifo.v13i2.a13194


Traffic monitoring system allows operators to monitor and analyze each traffic point via CCTV camera. 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 which one of the features is vehicle detection. Vehicle detection still poses a challenge especially when dealing with motorcycles that occupy the majority of the road in Indonesia. In this research, a motorcycle detection method using Haar-like features and Support Vector Machine (SVM) on CCTV camera image is proposed. A set of preprocessing procedure is performed on the input image before Haar-like features extraction. The features then classified using trained SVM model via sliding window technique to detect motorcycles. The test result shows 0.0 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 motorcycle from CCTV camera image.