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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 8, 2021.
Abstract: In this paper, we propose a robust real-time vehicle tracking and inter-vehicle distance estimation algorithm based on stereovision. Traffic images are captured by a stereoscopic system installed on the road, and then we detect moving vehicles with the YOLO V3 Deep Neural Network algorithm. Thus, the real-time video goes through an algorithm for stereoscopy-based measurement in order to estimate distance between detected vehicles. However, detecting the real-time objects have always been a challenging task because of occlusion, scale, illumination etc. Thus, many convolutional neural network models based on object detection were developed in recent years. But they cannot be used for real-time object analysis because of slow speed of recognition. The model which is performing excellent currently is the unified object detection model which is You Only Look Once (YOLO). But in our experiment, we have found that despite of having a very good detection precision, YOLO still has some limitations. YOLO processes every image separately even in a continuous video or frames. Because of this much important identification can be lost. So, after the vehicle detection and tracking, inter-vehicle distance estimation is done.
Omar BOURJA, Hatim DERROUZ, Hamd AIT ABDELALI, Abdelilah MAACH, Rachid OULAD HAJ THAMI and Francois BOURZEIX, “Real Time Vehicle Detection, Tracking, and Inter-vehicle Distance Estimation based on Stereovision and Deep Learning using YOLOv3” International Journal of Advanced Computer Science and Applications(IJACSA), 12(8), 2021. http://dx.doi.org/10.14569/IJACSA.2021.01208101
@article{BOURJA2021,
title = {Real Time Vehicle Detection, Tracking, and Inter-vehicle Distance Estimation based on Stereovision and Deep Learning using YOLOv3},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.01208101},
url = {http://dx.doi.org/10.14569/IJACSA.2021.01208101},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {8},
author = {Omar BOURJA and Hatim DERROUZ and Hamd AIT ABDELALI and Abdelilah MAACH and Rachid OULAD HAJ THAMI and Francois BOURZEIX}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.