HERMES License Plate Anonymizer
The CVEDA HERMES License Plate Anonymizer is a localization and blurring algorithm designed to consistently anonymize vehicle license plates. HERMES can be used in real-time, ensuring absolute compliance with GDPR regulation, or on video footage before public release. HERMES parameters are easily customizable according to project needs and local legislation.
Optimized for edge performance with a low power profile
Dawn to dusk; Artificial lighting
CVEDIA is a pioneer in the use of synthetic data for machine perception. We use deep learning to create synthetic-based detection and classification algorithms. CVEDIA models actually outperform traditional models because synthetic data allows for feature-based design. That means our models are safer and quicker to market. Today we work with over 30 of the world’s largest companies on their most complex deep learning projects.
Every model is backed by an ongoing maintenance service, as well as the ability to customize and add to a model through our in-house development team. We’ll even help you deploy our models in your software stack. Contact us for more information.
Backbone: Resnet-101, Detector: RefineDet
KERNEL INPUT SIZE
Each CVEDIA model comes backed with an ongoing maintenance agreement. Our team is available for continuous improvements and to ensure upkeep. Reach out to us for more information.
Fine Grain Classification
Speak to our team to discuss model classification as a service add on or as a separate model. CVEDIA’s in-house team will work with you to define your requirements, and build a synthetic-based model incorporating your classification specifications.
CVEDIA models are built using proprietary synthetic data technology – meaning adding additional classes to your model is possible in a matter of weeks.
Ensure your machine learning application complies with GDPR legislation in real-time. HERMES has a low power profile, making it simple to add to existing computing resources on intersection cameras, security systems, and vehicles.