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.
Low density
Medium Density
High Density
Fog
Rain
Snow
Low illumination
Night time
Distractor
Rich set of annotations: dots, approximate bounding boxes, blur-levels, etc.
Dots
Bounding Box
Blur-level
Dots
Bounding Box
Blur-level
Dots
Bounding Box
Blur-level
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.
Send an email to vishwanathsindagi@jhu.edu for any queries.