Road Lane and Traffic Density India

Citation Author(s):
JAGANNATHAN
J
Vellore Institute of Technology
Submitted by:
JAGANNATHAN J
Last updated:
Mon, 03/10/2025 - 07:03
DOI:
10.21227/2q43-nb93
Data Format:
.mov
License:
Creative Commons Attribution
431 Views
Categories:
Artificial Intelligence
IoT
Machine Learning
Transportation
Computer Vision
Keywords:
Autonomous Vehicles(AVs);deep learning;pedestrian detection;pedestrian anomaly behaviors; Video Dashcam dataset;
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Abstract 

The Dash Cam Video Dataset is a comprehensive collection of real-world road footage captured across various Indian roads, focusing on lane conditions and traffic dynamics. Indian roads are often characterized by inconsistent lane markings, unstructured traffic flow, and frequent obstructions, making lane detection and traffic identification a challenging task for autonomous vehicle systems. Reliable lane detection is crucial for developing robust Advanced Driver Assistance Systems (ADAS) and autonomous driving models tailored for Indian conditions. This dataset comprises high-quality dash cam videos capturing diverse road environments, including highways, city roads, and rural paths, under varying lighting and weather conditions. The dataset serves as a valuable resource for researchers and engineers working on computer vision-based traffic monitoring, lane detection algorithms, and autonomous vehicle navigation systems, helping to improve safety and efficiency in India's complex traffic ecosystem.

Instructions: 

Overview

The Dash Cam Video Dataset is a collection of real-world road footage captured from various locations in India, focusing on lane conditions and traffic scenarios. Given the unique challenges of Indian roads—such as inconsistent lane markings, unstructured traffic, and frequent obstructions—this dataset aims to support research in autonomous vehicle systems, lane detection algorithms, and traffic identification solutions.

Key Features

  • Real-world scenarios: Videos captured from urban, rural, and highway roads.

  • Challenging conditions: Includes footage with poor lane markings, dense traffic, and occlusions.

  • Diverse environments: Day and night recordings, different weather conditions, and varying road surfaces.

  • High-quality videos: Captured with high-resolution dash cameras for better clarity and accuracy.

Data Structure

  • Video Format: MOV

  • Resolution: 1080p 

  • Frame Rate: 30 FPS

  • Duration: 60 Seconds

  • Metadata: Includes timestamps

Usage Instructions

  1. Downloading the Dataset

    • The dataset is available as a ZIP file containing multiple video segments.

    • Extract the ZIP file to access individual video clips.

  2. Understanding the Data

    • Each video file is named based on its timestamp (e.g., FILE230827-043305-004851F.mov).

  3. Processing the Videos

    • Recommended tools: OpenCV, TensorFlow, PyTorch for lane detection and traffic classification.

    • Convert videos to image frames using OpenCV if needed.

    • Utilize deep learning models for segmentation and object detection.

  4. Potential Applications

    • Lane detection and departure warning systems.

    • Traffic density and congestion analysis.

    • Autonomous vehicle navigation research.

    • Road safety and accident prediction models.

Citation

If you use this dataset in your research, please cite it using the DOI

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