Identifying Traffic Direction via YOLOv8 and Cross Product Method

• Item type: Symposium(1)
• 公開日: 2024-12-27
• タイトル: Identifying Traffic Direction via YOLOv8 and Cross Product Method
• タイトル / 言語: en
• タイトル: Identifying Traffic Direction via YOLOv8 and Cross Product Method
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: Department of Computer Engineering, Faculty of Engineering, Khon Kaen, University
• 著者所属: Department of Computer Engineering, Faculty of Engineering, Khon Kaen, University
• 著者所属: Department of Computer Engineering, Faculty of Engineering, Khon Kaen, University
• 著者所属(英): en
• 著者所属(英): Department of Computer Engineering, Faculty of Engineering, Khon Kaen, University
• 著者所属(英): en
• 著者所属(英): Department of Computer Engineering, Faculty of Engineering, Khon Kaen, University
• 著者所属(英): en
• 著者所属(英): Department of Computer Engineering, Faculty of Engineering, Khon Kaen, University
• 著者名: Metawin, Sumethiwit; Chinpakorn, Waiyavudhi; Daranee, Hormdee
• 著者名(英): Metawin, Sumethiwit; Chinpakorn, Waiyavudhi; Daranee, Hormdee
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: This paper presents a novel approach to identifying traffic direction using a combination of the YOLOv8 object detection algorithm and a cross product-based method for vector analysis. YOLOv8, a state-of-the-art real-time object detection model, is employed to accurately detect and track vehicles in video streams. The detected vehicles are then analyzed using a Cross Product method, which calculates the relative movement vectors of the vehicles across consecutive frames. By determining the orientation of these vectors, the traffic direction―whether vehicles are moving left, right or forward―is inferred. This approach offers a robust solution for real-time traffic monitoring and analysis, leveraging the efficiency of YOLOv8 for object detection and the mathematical precision of the cross product for movement analysis. The proposed method is tested on various traffic scenarios, demonstrating its effectiveness in identifying traffic direction under diverse conditions.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: This paper presents a novel approach to identifying traffic direction using a combination of the YOLOv8 object detection algorithm and a cross product-based method for vector analysis. YOLOv8, a state-of-the-art real-time object detection model, is employed to accurately detect and track vehicles in video streams. The detected vehicles are then analyzed using a Cross Product method, which calculates the relative movement vectors of the vehicles across consecutive frames. By determining the orientation of these vectors, the traffic direction―whether vehicles are moving left, right or forward―is inferred. This approach offers a robust solution for real-time traffic monitoring and analysis, leveraging the efficiency of YOLOv8 for object detection and the mathematical precision of the cross product for movement analysis. The proposed method is tested on various traffic scenarios, demonstrating its effectiveness in identifying traffic direction under diverse conditions.
• 書誌情報: Proceedings of Asia Pacific Conference on Robot IoT System Development and Platform; 巻 2024, p. 57-58, 発行日 2024-12-27
• 出版者 / 言語: ja
• 出版者: 情報処理学会