Literature Review Application of YOLO Algorithm for Detection and Tracking

Feri Imanuel; Seven Kriston  Waruwu; Alvin  Linardy; Amir Mahmud  Husein

doi:10.47709/cnahpc.v6i3.4374

Authors

Feri Imanuel Universitas Prima Indonesia
Seven Kriston Waruwu Universitas Prima Indonesia
Alvin Linardy Universitas Prima Indonesia
Amir Mahmud Husein Universitas Prima Indonesia

DOI:

10.47709/cnahpc.v6i3.4374

Keywords:

Tracking, Detection, YOLO, Smart City, Vehicle

Dimension Badge Record

Abstract

A vehicle tracking system is a computer program that utilizes devices to monitor the position, movement and condition of a vehicle or fleet of vehicles. Multi-vehicle tracking on highways has significant research interest and practical value in building intelligent transportation systems. Nevertheless, traffic road video frames consist of various complex backgrounds and objects. Detection and tracking are very challenging because foreground to background switching occurs frequently. One-stage algorithm approaches such as YOLO and its various variants have been proven to be accurate for detecting vehicles. Meanwhile, the SORT, DeepSORT, ByteTrack and other algorithms can be combined in YOLO. The aim of this study is to highlight existing research on the application of YOLO and its variants in detecting and tracking vehicles, especially in traffic management. The journals used are limited to 2019 – 2024 and the journal sources consist of Hindawi, IEEE, MDPI, Research Gate, Science Direct, and Springer. Based on the research that has been reviewed, the YOLO variant algorithm approach has been successfully applied in the field of vehicle monitoring to support smart cities. In addition, many new model combinations and improvements have been proposed, proving that this algorithm has a big influence in the field of computer vision.

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