PEMROSESAN VIDEO UNTUK KLASIFIKASI JENIS KENDARAAN MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE; VIDEO PROCESSING FOR VEHICLE CLASSIFICATION USING SUPPORT VECTOR MACHINE ALGORITHM

IKA CANDRADEWI, Agus Harjoko

2015 | Disertasi | FMIPA

In the automation of vehicle traffic monitoring system, information about the type of vehicle, it is important because used in the process of further analysis as predictive traffic flow to detect traffic congestion and management of traffic control lights. Currently calculation of the number of vehicles is still done manually. Computer vision applied to traffic monitoring systems could present data more complete and uptodate. In this study consists of three main stages, namely Classification, Feature Extraction and Detection. At stage vehicle classification used klasifikasi multi kelas SVM method to evaluate characteristics of the object into 8 classes (LV-Small Trucks, LV-Mobil, LV-Mikrobis, MHV-Trucks Medium, Medium-Bus MHV, HV-LB (Big Bus), HV- LT (Large Trucks), MC-Motor). Features are obtained from the detection object, and processed on feature extraction stage to get features of geometry, HOG and LBP. In the detection stage of the vehicle used MOG method combined with object detection method (HOG-SVM) to get an object in the form of a moving vehicle and does not move. SVM has the advantage of detail and based statistical computing. Geometry, HOG and LBP characterize complex and represents an object in the form of the gradient and local histogram. The test results demonstrate the accuracy of the calculation of the number of vehicles at the stage of vehicle detection is 93,76 %, with the parameters HOG cellSize 4x4, 2x2 blocksize, the son of vehicle classification 9. The test results give the overall mean recognition rate 91,31 %, mean precision rate 77,32 %, and mean recall rate 75,66 %

Kata Kunci : LBP; MOG; MBF; feature extraction; vehicle classification, vehicle detectiom; Support Vector Machine