Vehicle tracking and speed estimation under mixed traffic conditions using YOLOV4 and sort: a case study of Hanoi

• Autorzy: Vuong Xuan Can , Mou Rui-Fang , Vu Trong Thuat

Identyfikatory

DOI

10.20858/tp.2022.17.4.02

• Języki publikacji: EN

Abstrakty

EN

This paper presents a method to estimate vehicle speed automatically, including cars and motorcycles under mixed traffic conditions from video sequences acquired with stationary cameras in Hanoi City of Vietnam. The motion of the vehicle is detected and tracked along the frames of the video sequences using YOLOv4 and SORT algorithms with a custom dataset. In the method, the distance traveled by the vehicle is the length of virtual point-detectors, and the travel time of the vehicle is calculated using the movement of the centroid over the entrance and exit of virtual point-detectors (i.e., region of interest), and then the speed is also estimated based on the traveled distance and the travel time. The results of two experimental studies showed that the proposed method had small values of MAPE (within 3%), proving that the proposed method is reliable and accurate for application in real-world mixed traffic environments like Hanoi, Vietnam.

Słowa kluczowe

EN

vehicle tracking; speed estimation; mixed traffic conditions; YOLO; SORT

PL

śledzenie pojazdu; szacowanie prędkości; warunki ruchu mieszanego; YOLO; SORT

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Transport Problems
• Rocznik: 2022
• Tom: T. 17, z. 4
• Strony: 17--26
• Opis fizyczny: Bibliogr. 35 poz.

Twórcy

autor

Vuong Xuan Can

• University of Transport and Communications, Faculty of Environment and Transport Safety; Hanoi, Vietnam, 100000

• https://orcid.org/0000-0002-1174-1416

autor

Mou Rui-Fang

• Southwest Jiaotong University, School of Transportation and Logistics; Chengdu, China, 611756

• https://orcid.org/0000-0002-9191-4355

autor

Vu Trong Thuat

• University of Transport and Communications, Faculty of Electrical-Electronic Engineering; Hanoi, Vietnam, 100000

• https://orcid.org/0000-0002-2363-3519

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).