Traffic congestion Dataset

The main aim of this dataset is to enable detection of traffic congestion from surveillance cameras using one-stage object detectors. The dataset contains congested and uncongested traffic scenes with their respective labels. This dataset is collected from different surveillance cameras video footage. To prepare the dataset frames are extracted from video sources and resized to a dimension of 500 x 500 with .jpg image format. To Annotate, the image LabelImg software has used. The format of the label is .txt with the same name as the image. The dataset is mainly prepared for YOLO Models but it can be converted to other models format.

本数据集的核心目标在于支持通过单阶段目标检测器（one-stage object detectors）从监控摄像头采集的画面中实现交通拥堵检测。该数据集涵盖拥堵与非拥堵两类交通场景，并附带对应的标注标签。本数据集的数据采集自多台监控摄像头的视频录像片段。为完成数据集制备，需从视频源中提取图像帧，并将其统一调整至500×500像素的尺寸，存储为JPEG（JPEG）格式图像。标注环节采用LabelImg软件对图像进行标注，标注文件的格式为TXT，且其文件名与对应图像的文件名完全一致。本数据集主要面向YOLO模型构建，但也可转换为其他模型所需的标注格式。

搜集汇总

背景与挑战

背景概述

该数据集包含从监控摄像头视频中提取的交通拥堵和非拥堵场景图像，图像尺寸为500x500，标注格式为.txt，主要用于训练YOLO模型进行交通拥堵检测。