Traffic congestion Dataset

Published: 2 November 2020| Version 1 | DOI: 10.17632/wtp4ssmwsd.1
Bedada Bekele


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.


Steps to reproduce

The dataset is collect from different surveillance cameras video footage which contains congested and uncongested traffic scenes. To extract images from video OpenCV software used. After extraction all images are annotated using LabelImg. Bounding box annotation types are used to generate a text(.txt) labels for each images with same name as image.


Addis Ababa Science and Technology University


Image Database