A comprehensive dataset with 4,372 images and 1.51 million annotations. In comparison to existing datasets, the proposed dataset is collected under a variety of diverse scenarios and environmental conditions. In addition, the dataset provides comparatively richer set of annotations like dots, approximate bounding boxes, blur levels, etc.
Includes several images with weather-based degradations and illumination variations, making it a very challenging dataset. Additionally, the dataset consists of rich annotations at both image-level and head-level.
Contains 4,372 images (with an avg resolution of 1430x910) collected under a diverse set of conditions and various geographical locations.
Specific care is taken to improve diversity of the dataset by including images under adverse weather and various illumination conditions.
Contains a total of 1.51 million dot annotations with an average of 346 dots per image and a maximum of 25K dots.
Provides head-level labels (dots, approx. bounding box, blur-level, etc.) and image-level labels (scene type and weather condition).
Diverse Conditions: varying densities, illumination variations, adverse weather conditions such as fog, rain and snow.
Rich set of annotations: dots, approximate bounding boxes, blur-levels, etc.
Distribution of image labels
Distribution of different density images
Distribution of weather-degradations
If you find this dataset useful, please consider citing the following work:
We would like to express our deep gratitude to everyone who contributed to the creation of this dataset including the members of the JHU-VIU lab and the numerous Amazon Mturk workers. We would like to specially thank Kumar Siddhanth, Poojan Oza, A. N. Sindagi, Jayadev S, Supriya S, Shruthi S and S. Sreevali for providing assistance in annotation and verification efforts.
Lastly, we would like to thank Kannan Kandappan for the landing page design.
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