Dataset for Pedestrian Traffic Lights detection

Published: 4 January 2021| Version 1 | DOI: 10.17632/9tm59d3nsn.1
Somaiya Khan, Ali Khan


The dataset contains a total of 809 raw images , where 441 images are of red pedestrian light and 368 images belong to green pedestrian light class. The dataset is created by scanning through various search engines by using different keywords that include pedestrian crosswalk light, pedestrian green light, pedestrian lights, pedestrian red light, pedestrian traffic light and so on. We tried to consider versatile images in both classes for creating a certain degree of inter-class confusion in order to better train the model. The dataset is designed for binary problem of red or green pedestrian traffic lights detection in the city landscape. The dataset is divided into 80:20 for training and testing purposes in our study. The dataset can be used by the prospective researchers to propose machine learning algorithms for automated detection and screening of jaywalkers towards ensuring the efficient traffic monitoring system and performing surveillance in smart cities.



Computer Vision, Object Detection, Traffic Management System, Smart City, Surveillance