Labeling videos for affect and facial expression is tedious and time consuming. Researchers often spend significant amounts of time annotating experimental data, or simply lack the time required to label their data. For these reasons we have developed VidL, a video labeling system that is able to harness the distributed manpower of the internet. VidL allows for the management of large amounts of data and a distributed work force from a central location. The VidL application can help you get the data you need. As an example, we recently labeled 700 short videos, approximately 60 hours of work, in 2 days using 20 labelers working from their own computers.
VidL is a server based application framework, intended for the scientific community, for the review and annotation of video. The VidL framework is intended to be used on a web-server, which acts as a central repository for the storage of video and the VidL application. This allows for video coders to work remotely annotating video, while a single administrator can control access, application appearance, and check coder reliability remotely.
- Label videos quickly and reliably
- Open source
- Web-based application allows for large distributed workforce
- Centralized administration for control of data and application maintenance
- Customizable meta-labels allow for relevant video annotations
- Visualization allows user to easily view annotations
- Compare annotations between labelers for any video
- Calculate statistics
- Monitor quality of labels and keep track of worker hours and progresss