A Solution for Automatic Counting and Differentiate Motorcycles and Modified Motorcycles in Remote Area

Motorcycles are the most significant contributor to the vehicle numbers in Indonesia, about 81% of all vehicles in the country. In addition, the growth of modified motorcycles has also increased in several areas, particularly remote places. Many studies have been conducted for detecting vehicles. However, most vehicle detection studies were conducted to detect cars or four-wheeled vehicles, and only a few studies were done to detect motorcycles. Further problems increase if the system is implemented in remote areas with limited electricity power resources that need low-cost budget specification computation. This study detects and calculates the number of motor vehicles and modified motorcycles passed on a highway from video data. It proposed Machine Learning instead of Deep Learning to suit the low computational video in remote areas. Computer vision-based methods used in the prediction are optical flow and Histogram Oriented Gradient (HOG) + Support Vector Machine (SVM). Five videos were used in the system testing, taken from the roadsides using a static camera with a resolution of 160x112 pixels at ±135º angle. This research showed that the accuracy of motorcycles and modified motorcycles detection and calculation systems using the HOG + SVM method is higher than the optical flow method. The average accuracy of HOG + SVM for motorcycles and modified motorcycles is 89.70% and 95.16%, respectively.

@article{Indrabayu2022,
  title     = {A Solution for Automatic Counting and Differentiate Motorcycles and Modified Motorcycles in Remote Area},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {2},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Indrabayu and Intan Sari Areni and Anugrayani Bustamin and Elly Warni and Sofyan Tandungan and Rizka Irianty and Najiah Nurul Afifah},
  doi       = {10.14569/IJACSA.2022.0130217},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130217}
}