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Journal : Proceeding International Applied Business and Engineering Conference

Detection of Congested Traffic Flow during Road Construction using Improved Background Subtraction with Two Levels RoI Definition Yoanda Alim Syahbana; Yasunari Yokota
International ABEC 2021: Proceeding International Applied Business and Engineering Conference 2021
Publisher : International ABEC

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Abstract

This study is aimed to detect traffic congestion that may occur during roadblocks of road construction. We improved the background subtraction method by considering Region of Interest (RoI) in the video frame to detect the congestion. The proposed method has experimented with video test material that shows traffic condition in the road construction site. The performance of the proposed method is evaluated using Confusion Matrix by comparing the result of the experiment with ground truth obtained visually. As a benchmarking process, the performance is also compared with the conventional background subtraction method. The result shows that the proposed method can achieve an accuracy of 83.2% for video from the first camera and 82.3% for video from the second camera. In comparison, the conventional background subtraction method only achieves 49.8% for video from the first camera and 0% for video from the second camera. Based on this evaluation, the proposed method can support implementation of efficient traffic control using adaptive traffic light that is equipped with camera.
Object Detection And Monitor System For Building Security Based On Internet Of Things (IoT) Using Illumination Invariant Face Recognition Ivan Chatisa; Yoanda Alim Syahbana; Agus Urip Ari Wibowo
International ABEC Vol. 2 (2022): Proceeding International Applied Business and Engineering Conference 2022
Publisher : International ABEC

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Abstract

Theft, burglary and intrusion are criminal acts that often occur in the environment when there are opportunity or negligence made by the owner and security officers. Many studies have been carried out to improve environmental security by applying cameras as a surveillance medium. However, the camera is still not optimal at detecting objects if the environment is in poor lighting conditions (dark). Therefore, in this study, a monitoring and object detection system was built by applying the Illumination Invariant model. Illumination Invariant model that is used to improve the appearance of object images from light and shadow reflections. In this study, the detection process and objects are carried out using human facial features captured by the camera. The camera used is a Logitec C270 Webcam HD 720p via the USB port on the Raspberry Pi. Raspberry Pi processes human face image data and sends the results of data processing to a MySQL database using the HTTP Protocol. The process of sending data is done with the concept of API (Application Programming Interface) using Python Flask. In this study, all tests were carried out on the system using black box testing techniques with the results of the functional requirements being successfully executed 100%. In this study, testing the object detection feature based on different lighting conditions. The test was carried out 15 times by comparing the original image and the results of the implementation of the Illumination Invariant model. Based on the test results by applying the illumination of the Invariant model, the quality of object detection accuracy is 86.7%.
Early Detection and Tracking of Distant Incoming Traffic using Improved Detection on Road Vanishing Point Reference for Adaptive Traffic Light Signaling Yoanda Alim Syahbana; Yokota Yasunari
International ABEC Vol. 2 (2022): Proceeding International Applied Business and Engineering Conference 2022
Publisher : International ABEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (938.846 KB)

Abstract

Real-time monitoring is essential and influences the decision-making process of adaptive traffic light systems. During temporary road closures, only one side of the lane can be accessed, increasing the need to recognize and track oncoming vehicles. Therefore, it is crucial to detect oncoming vehicles that are far away as early as possible, as waiting for an oncoming vehicle near a traffic light may delay the signal, leading to sudden braking or an accident. The purpose of this study was to improve traffic detection and tracking, even when the traffic is still far from the traffic lights. Vanishing point as detection reference is estimated, and Region of Interest (RoI) is calculated. An evaluation is performed based on how quickly the proposed method detects oncoming traffic compared to the R-CNN method. The results show that the proposed method requires an average of 17.75 frames to detect the target vehicle, while R-CNN requires an average of 63.36 frames to detect the target vehicle. The results show that the accuracy of the proposed method depends on the number of pixel orientations when estimating the vanishing point and how accurately the RoI is defined. Therefore, the proposed method reliably supports the safety and reliability of adaptive traffic light systems.