视频WeAther识别 (VARG):一个强度标记的视频天气识别数据集
Himanshu Gupta1, Oleksandr Kotlyar1, Henrik Andreasson1
1Centre for Applied Autonomous Sensor Systems, Örebro University, 701 82 Örebro, Sweden.
Journal of imaging
|November 26, 2024
概括
这项研究介绍了VARG,这是一个新的视频数据集,用于识别恶劣的天气,如雨,雾和雪,包括强度水平. 这对于改善自动驾驶系统中的计算机视觉至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 恶劣的天气条件 (雨,雪,雾) 显著降低了计算机视觉系统的性能.
- 准确的天气识别对于农业和运输领域的自主系统的安全性和稳定性至关重要.
- 现有的数据集缺乏关键的天气强度标签,阻碍了模型开发.
研究的目的:
- 介绍VARG,一个新的基于视频的数据集,用于天气识别和强度标签.
- 为训练和评估不利天气检测模型提供全面的资源.
- 为了解决缺乏天气强度信息的现有数据集的局限性.
主要方法:
- 收集并策划了来自社交媒体和作者录音的各种视频序列.
- 将视频处理成有注释的剪辑,按天气类型 (雨,雾,雪) 和强度 (没有,中等,高) 分类.
- 开发了两套用于培训的注释集:多标签天气强度分类和多类天气场景分类.
主要成果:
- VARG数据集包含来自1079个视频的6742个注释片段,分为培训 (5159个片段) 和测试 (1583个片段) 集.
- 该数据集支持用于天气识别的多标签和多类分类任务.
- 一项评估研究证明了数据集在基于深度学习的视频识别方法中的实用性.
结论:
- VARG是推进自主系统天气感知计算机视觉研究的宝贵资源.
- 强度标签的加入增强了模拟天气对传感器数据的影响的能力.
- 这一数据集有助于在具有挑战性的天气条件下开发更具弹性自主技术.
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