Environmental and behavioral quantification based on video data are becoming more widely and cost-effectively applicable due to advances in Deep Learning. One of the main barriers to adoption of the new technologies is the difficulty of providing sample data of sufficient quality and quantity for machine learning techniques. This project makes expert annotation of videos more efficient. 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 etc.
In order to tap the potential of video data in the traffic sector, Palaimon is developing software for the efficient annotation of video data specifically for the traffic sector in this project. With the efficient annotation of video data, this project creates the basis on which AI can be optimally used as a key technology in the traffic sector. The development as open source software ensures that the results can be applied in the broad public.
Within the targeted project scope and project volume, a representative use case in the transportation sector will be extracted, on the basis of which the annotation software will be developed iteratively. It will be 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 on BMVI 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|