CamVid - Motion-based Segmentation and Recognition Dataset

The Cambridge-driving Labeled Video Database (CamVid) is the first collection of videos with object class semantic labels, complete with metadata. The database provides ground truth labels that associate each pixel with one of 32 semantic classes. The database addresses the need for experimental data to quantitatively evaluate emerging algorithms. While most videos are filmed with fixed-position CCTV-style cameras, our data was captured from the perspective of a driving automobile. The driving scenario increases the number and heterogeneity of the observed object classes. White paper - http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/images/CGVI/Gabriel1.pdf

Data and Resources

Additional Info

Field Value
Author Gabriel J. Brostow, Jamie Shotton, Julien Fauqueur and Roberto Cipolla
Maintainer Gabriel Brostow
Number of Instances 4
Package Description The Cambridge-driving Labeled Video Database (CamVid) is the first collection of videos with object class semantic labels, complete with metadata. The database provides ground truth labels that associate each pixel with one of 32 semantic classes.
Dataset has missing values False