The solution is a standalone web application, which consists of 2 modules — Admin Panel and Workspace. Depending on the user’s role on the project, it allows performing specific actions. There are 5 user’s roles — Superadmin, Coordinator, Observer, Labeling Manager, and Labeler. The main functional responsibilities are shared between them.
A Labeler is able to:
- Annotate the objects with a geometric figure-shaped masks (triangle, circle, rectangular, trapezoid, etc.)
- Tag the objects
- Group the objects into relevant categories
Superadmin has the authority to:
- Manage organizations, users and labeling groups
- Upload videos
- Assign project roles, etc.
Coordinator is responsible for project and task management.
Observer and Labeling Manager control the labelers’ performance and project quality.
A labeler receives specific instructions in an .xml format. They contain the information on the objects, which require labeling (vehicles, pedestrians, road marking, road signs, etc). The files are stored at a remote media storage. The labeler manually annotates the objects, which have not been identified through the Machine Learning algorithms.
Currently, the app supports only low color depth videos, which means that the imported video files must be processed before the labeling. To prepare the video frames for manual labeling, the solution utilizes convolutional neural networks-based image preprocessing algorithms.
