license: cc-by-nc-nd-4.0
task_categories:
- object-detection
tags:
- Auto
- Computer Vision
- Machine learning
- Smart Cities
- bbox
- urban traffic
size_categories:
- n<1K
Traffic Dataset - 500 Videos
Dataset comprises 500 videos of urban traffic captured by surveillance cameras, providing real-time traffic data enriched with bounding box annotations for vehicles and pedestrians. Designed for traffic monitoring and safety research, the dataset supports tasks like vehicle detection, traffic flow analysis, and accident prediction.
By leveraging this dataset, researchers and engineers can advance real-time object detection, traffic surveillance systems, and intelligent transportation solutions.- Get the data
Each frame is meticulously labeled in COCO-style JSON format, enabling seamless integration with object detection and tracking pipelines.With its focus on two primary object categories - cars and pedestrians - this dataset serves as a practical resource for developing and testing object detection and tracking algorithms in traffic scenarios.
Frequently Asked Questions
Who can benefit from this real-time traffic video dataset?
This dataset can benefit computer-vision researchers, intelligent transportation system developers, traffic-management teams, and autonomous-driving engineers. Its annotated urban scenes are particularly relevant to teams developing vehicle and pedestrian detection, multi-object tracking, traffic-flow analysis, and incident-monitoring systems.
Can this dataset be used for smart-city research?
Yes. The dataset is well suited to smart-city research involving automated transportation monitoring and urban mobility analytics. Vehicle and pedestrian detections can be transformed into higher-level measurements such as traffic volume, road-user activity, and intersection utilization.
What makes the data useful for traffic video analysis?
The dataset combines several information layers: high-resolution urban video, object-level bounding boxes, and contextual traffic metadata. This allows researchers to approach the same recordings from different perspectives, ranging from conventional object detection to traffic analytics and temporal modeling. The fixed viewpoint provides a consistent scene geometry, while long recordings expose models to changing traffic conditions. Environmental information such as weather and time of day adds another dimension for robustness testing.
💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.
1920×1080 resolution across all frames facilitates reliable model training and evaluation for applications such as traffic flow optimization, pedestrian safety systems, and autonomous vehicle development.