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Video WeAther RecoGnition (VARG): An Intensity-Labeled Video Weather Recognition Dataset.

Himanshu Gupta1, Oleksandr Kotlyar1, Henrik Andreasson1

  • 1Centre for Applied Autonomous Sensor Systems, Örebro University, 701 82 Örebro, Sweden.

Journal of Imaging
|November 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces VARG, a new video dataset for recognizing adverse weather like rain, fog, and snow, including intensity levels. This is crucial for improving computer vision in autonomous systems.

Keywords:
video classificationweather detectionweather intensity classification

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Adverse weather conditions (rain, snow, fog) significantly degrade computer vision system performance.
  • Accurate weather recognition is vital for the safety and robustness of autonomous systems in agriculture and transportation.
  • Existing datasets lack crucial weather intensity labels, hindering model development.

Purpose of the Study:

  • To introduce VARG, a novel video-based dataset for weather recognition with intensity labels.
  • To provide a comprehensive resource for training and evaluating models for adverse weather detection.
  • To address the limitations of existing datasets lacking weather intensity information.

Main Methods:

  • Collected and curated diverse video sequences from social media and author recordings.
  • Processed videos into annotated clips categorized by weather type (rain, fog, snow) and intensity (absent, moderate, high).
  • Developed two annotation sets for training: multi-label weather intensity classification and multi-class weather scenario classification.

Main Results:

  • The VARG dataset contains 6742 annotated clips from 1079 videos, split into training (5159 clips) and testing (1583 clips) sets.
  • The dataset supports both multi-label and multi-class classification tasks for weather recognition.
  • An evaluation study demonstrated the dataset's utility with deep learning-based video recognition approaches.

Conclusions:

  • VARG is a valuable resource for advancing research in weather-aware computer vision for autonomous systems.
  • The inclusion of intensity labels enhances the capability to model weather's impact on sensor data.
  • This dataset facilitates the development of more resilient autonomous technologies in challenging weather conditions.