LabelMe video: Building a video database with human annotations
Name
Torralba_LabelMe video.pdf
Size
3.9 MB
Format
Adobe PDF
Checksum (MD5)
26306ab01b85540be38a6b523de0ea58
Author(s)
Yuen, Jenny
Russell, Bryan
Liu, Ce
Torralba, Antonio
Date Issued
September 2009
Journal
IEEE International Conference on Computer Vision. (12th, 2009)
Publisher
Institute of Electrical and Electronics Engineers
Citation
Yuen, J. et al. “LabelMe Video: Building a Video Database with Human Annotations.” Computer Vision, 2009 IEEE 12th International Conference On. 2009. 1451-1458. Copyright © 2009, IEEE
Version
Final published version
Abstract
Currently, video analysis algorithms suffer from lack of information regarding the objects present, their interactions, as well as from missing comprehensive annotated video databases for benchmarking. We designed an online and openly accessible video annotation system that allows anyone with a browser and internet access to efficiently annotate object category, shape, motion, and activity information in real-world videos. The annotations are also complemented with knowledge from static image databases to infer occlusion and depth information. Using this system, we have built a scalable video database composed of diverse video samples and paired with human-guided annotations. We complement this paper demonstrating potential uses of this database by studying motion statistics as well as cause-effect motion relationships between objects.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICCV.2009.5459289
