LabelMe video: Building a video database with human annotations
Author(s)
Yuen, Jenny; Russell, Bryan; Liu, Ce; Torralba, Antonio
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Show full item recordAbstract
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.
Date issued
2009-09Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
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
Other identifiers
INSPEC Accession Number: 11367750
ISBN
978-1-4244-4420-5
ISSN
1550-5499