OS-VAT (01/21 – 12/21)
Open Source Video Annotation Tool - OS-VAT
Problem Statement
Due to advances in Deep Learning, environmental and behavioral quantification based on video data becomes widely used and more cost-efficient. One of the main barriers in the adoption of these new technologies is the difficulty to provide sufficient high-quality sample data to train machine learning algorithms. This project advances efficient video annotation by expert users. By focusing on example data from the traffic and urban domain, it will especially support measures in traffic and urban planning. e.g. for cyclists, e-scooters, and pedestrians.
Project Goal
In order to utilize the potential of video data in the traffic sector, Palaimon develops software for the efficient annotation of video data, specifically for traffic monitoring purposes. By enabling an efficient annotation of video data, this project creates a basis on which AI can optimally by used as a key technology in the traffic sector. The development as open source software ensures that the results are accessible to the broad public, can be applied in other projects, and extended and customized by users.
Project Approach
Within the targeted project scope and project volume, a representative use case in the transportation sector is extracted. On this basis the annotation software is developed iteratively. It is examined into which subtasks the annotation process can be decomposed in order to create the best possible annotation environment for humans. Close cooperation with relevant stakeholders, such as the associated partners Autobahn GmbH des Bundes and the Ministry of Transport Baden-Württemberg, ensures that the software is developed to meet the needs of the target group.
Project Details
- Project on BMDV website
- FKZ: 19F2160A
- Project Volume: 71.226 €
- Project Duration: 01/2021 – 12/2021
- Associated Partner:
| Ministerium für Verkehr Baden-Württemberg - Referat 24: Erhaltung und Ingenieurbau | Die Autobahn GmbH des Bundes, Berlin |
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