Nighttime Motorcycle Detection for Sparse Traffic Images Using Machine Learning
DOI:
https://doi.org/10.21512/commit.v17i1.8443Keywords:
Nighttime Motorcycle Detection, Sparse Traffic Images, Machine LearningAbstract
Traffic accidents often occur at night. It is understandable, since at night, people have low visibility. Many efforts to develop tools to detect nearby vehicles to avoid crashes have been reported. However, most of them worked only on detecting cars. The research aims to detect motorcycles at night, to complement the previous studies, which mainly focused on cars. The research introduces four features which are extracted from the red pixel and edge map. The algorithm to extract the features has also been developed. They are applied to three commonly used classifiers: Artificial Neural Network (ANN), Decision Tree, and Support Vector Machine (SVM) classifiers to validate the effectiveness of the features. Since the public dataset related to the research is not available yet, the nighttime videos from YouTube have been collected. The datasets contain all the various levels of darkness. They are divided into an 80-20 ratio for training and testing sets to support the experiment and measure the validity of the proposed method. As the best result, the detection using ANN can detect motorcycle proposals with accuracy of 72.71%, precision of 65.10% and recall of 73.33%. Furthermore, during the experiment, the classification can perform consistently in 0.04 seconds per image. Therefore, the method is suitable for use in a real-time system.
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