为了提高智能交通系统中的监控准确性和实时性,解决传统方法在复杂环境中的局限。本文结合YOLOv8和ByteTrack算法,提出一种新的车辆轨迹检测技术。YOLOv8提高了检测速度和准确率,ByteTrack通过深度学习有效跟踪车辆。首先使用YOLOv8算...为了提高智能交通系统中的监控准确性和实时性,解决传统方法在复杂环境中的局限。本文结合YOLOv8和ByteTrack算法,提出一种新的车辆轨迹检测技术。YOLOv8提高了检测速度和准确率,ByteTrack通过深度学习有效跟踪车辆。首先使用YOLOv8算法对视频流中的每一帧进行实时目标检测,以识别和定位车辆;然后,利用ByteTrack算法对检测到的车辆进行特征提取和运动轨迹跟踪,维持车辆在连续帧中的一致性。为智能交通系统提供了一种技术手段。This paper aims to improve the monitoring accuracy and real-time performance in intelligent transportation systems, and solve the limitations of traditional methods in complex environments. Combining YOLOv8 and ByteTrack algorithms, this paper proposes a new vehicle trajectory detection technology. YOLOv8 improves the detection speed and accuracy, while ByteTrack effectively tracks vehicles through deep learning. Firstly, YOLOv8 algorithm is used for each frame of video streaming real-time target detection, in order to identify and locate the vehicle;Then, ByteTrack algorithm is used to extract the features and track the motion trajectory of the detected vehicles to maintain the consistency of the vehicles in consecutive frames. It provides a technical method for the intelligent transportation system.展开更多
文摘为了提高智能交通系统中的监控准确性和实时性,解决传统方法在复杂环境中的局限。本文结合YOLOv8和ByteTrack算法,提出一种新的车辆轨迹检测技术。YOLOv8提高了检测速度和准确率,ByteTrack通过深度学习有效跟踪车辆。首先使用YOLOv8算法对视频流中的每一帧进行实时目标检测,以识别和定位车辆;然后,利用ByteTrack算法对检测到的车辆进行特征提取和运动轨迹跟踪,维持车辆在连续帧中的一致性。为智能交通系统提供了一种技术手段。This paper aims to improve the monitoring accuracy and real-time performance in intelligent transportation systems, and solve the limitations of traditional methods in complex environments. Combining YOLOv8 and ByteTrack algorithms, this paper proposes a new vehicle trajectory detection technology. YOLOv8 improves the detection speed and accuracy, while ByteTrack effectively tracks vehicles through deep learning. Firstly, YOLOv8 algorithm is used for each frame of video streaming real-time target detection, in order to identify and locate the vehicle;Then, ByteTrack algorithm is used to extract the features and track the motion trajectory of the detected vehicles to maintain the consistency of the vehicles in consecutive frames. It provides a technical method for the intelligent transportation system.
