Projects per year
Description
Rain, Snow, and Bad Weather in Traffic Surveillance
Computed vision-based image analysis lays the foundation for automatic traffic surveillance. This works well in daylight when the road users are clearly visible to the camera but often struggles when the visibility of the scene is impaired by insufficient lighting or bad weather conditions such as rain, snow, haze, and fog.
In this dataset, we have focused on collecting traffic surveillance video in rainfall and snowfall, capturing 22 five-minute videos from seven different traffic intersections. The illumination of the scenes vary from broad daylight to twilight and night. The scenes feature glare from headlights of cars, reflections from puddles, and blur from raindrops at the camera lens.
We have collected the data using a conventional RGB colour camera and a thermal infrared camera. If combined, these modalities should enable robust detection and classification of road users even under challenging weather conditions.
100 frames have been selected randomly from each five-minute sequence and any road user in these frames is annotated on a per-pixel, instance-level with corresponding category label. In total, 2,200 frames are annotated, containing 13,297 objects.
Computed vision-based image analysis lays the foundation for automatic traffic surveillance. This works well in daylight when the road users are clearly visible to the camera but often struggles when the visibility of the scene is impaired by insufficient lighting or bad weather conditions such as rain, snow, haze, and fog.
In this dataset, we have focused on collecting traffic surveillance video in rainfall and snowfall, capturing 22 five-minute videos from seven different traffic intersections. The illumination of the scenes vary from broad daylight to twilight and night. The scenes feature glare from headlights of cars, reflections from puddles, and blur from raindrops at the camera lens.
We have collected the data using a conventional RGB colour camera and a thermal infrared camera. If combined, these modalities should enable robust detection and classification of road users even under challenging weather conditions.
100 frames have been selected randomly from each five-minute sequence and any road user in these frames is annotated on a per-pixel, instance-level with corresponding category label. In total, 2,200 frames are annotated, containing 13,297 objects.
Date made available | 1 Jan 2018 |
---|---|
Publisher | Kaggle |
Date of data production | 2018 |
Projects
- 1 Finished
-
InDeV: In-Depth understanding of accident causation for Vulnarable road users
Andersen, C. S., Lahrmann, H., Madsen, T. K. O., Møller, K. M., Jensen, M. B., Bahnsen, C. H., Moeslund, T. B. & Christensen, M. B.
01/05/2015 → 31/10/2018
Project: Research
Research output
- 1 Journal article
-
Rain Removal in Traffic Surveillance: Does it Matter?
Bahnsen, C. H. & Moeslund, T. B., Aug 2019, In: I E E E Transactions on Intelligent Transportation Systems. 20, 8, p. 2802-2819 18 p., 8510919.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile62 Citations (Scopus)636 Downloads (Pure)