Improved Accuracy of Vehicle Counter for Real-Time Traffic Monitoring System

Author:

Setiadi De Rosal Ignatius Moses1,Fratama Rizki Ramadhan1,Partiningsih Nurul Diyah Ayu1

Affiliation:

1. Department of Informatics Engineering , Dian Nuswantoro University , 207 Imam Bonjol Street, Semarang 50131, Indonesia

Abstract

Abstract This research proposes a background subtraction method with the truncate threshold to improve the accuracy of vehicle detection and tracking in real-time video streams. In previous research, vehicle detection accuracy still needs to be optimized, so it needed to be improved. In the vehicle detection method, there are several parts that greatly affect, one of which is the thresholding technique. Different thresholding methods can affect the results of the background and foreground separation. Based on the results of testing the proposed method can improve accuracy by more than 20% compared to the previous method. The thresholding method has a considerable influence on the final result of vehicle object detection. The results of the average accuracy of the three types of time, i.e. morning, daytime, and afternoon reached 96.01%. These results indicate that the vehicle counting accuracy is very satisfying, moreover, the method has also been implemented in a real way and can run smoothly.

Publisher

Walter de Gruyter GmbH

Subject

Computer Science Applications,General Engineering

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Efficient Feature Extraction Method for Traffic Surveillance in Intelligent Transportation Systems;Advances in Computational Intelligence and Robotics;2024-05-31

2. Adaptive traffic light control using vision-based deep learning for vehicle density estimation;Proceedings of the 2024 6th Asia Pacific Information Technology Conference;2024-01-29

3. Moving Vehicle Detection Combining Edge Detection and Gaussian Mixture Models;Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery;2022

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