<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Improved Accuracy of Vehicle Counter for Real-Time Traffic Monitoring System"}

This research proposes a background subtraction method with the truncate threshold to improve the accuracy of vehicle detection and tracking in real-time video...

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

Online veröffentlicht: 30. Apr. 2020

Seitenbereich: 125 - 133

DOI: https://doi.org/10.2478/ttj-2020-0010

Schlüsselwörter
Traffic Monitoring, Background Subtraction, Truncate Threshold, Real-time tracking, Vehicle Detection

© 2020 De Rosal Ignatius Moses Setiadi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Online veröffentlicht: 30. Apr. 2020

Seitenbereich: 125 - 133

DOI: https://doi.org/10.2478/ttj-2020-0010

SchlüsselwörterTraffic Monitoring, Background Subtraction, Truncate Threshold, Real-time tracking, Vehicle Detection

© 2020 De Rosal Ignatius Moses Setiadi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Traffic Monitoring, Background Subtraction, Truncate Threshold, Real-time tracking, Vehicle Detection