Deep learning models for Vision AI require extensive, high-quality datasets. However, preparing these datasets is time-consuming and labor-intensive, involving frame-by-frame video labeling to ensure the accuracy needed for real-world applications. The quality of training data is critical to achieving reliable AI performance.
Developing Vision AI systems often faces significant hurdles due to the scarcity of source data. Rare events, like anomalies or accidents, are challenging to capture, and privacy concerns further restrict access to sensitive footage. These limitations make it difficult to build robust datasets for deep learning.
Synthetic data offers a powerful way to address these issues. By leveraging gaming technology and generative AI, we can create realistic virtual environments to generate training data.
High-quality synthetic visual data optimized for AI training, ensuring perfect segmentation, labeling, and privacy compliance.
Unlock deep customization of video analytics using digital twin technology to simulate any environment. Our synthetic data generation replicates real-world edge conditions, allowing us to develop and optimize solutions tailored to your needs.
Capture a comprehensive view of shared environments with multiple cameras operating simultaneously from different angles. Perfect for AI vision models requiring distributed situational awareness and seamless integration into multi-camera setups.
Simulate cameras mounted on moving platforms like drones, cars, or robotic devices. Supports both fixed and Pan-Tilt-Zoom (PTZ) cameras for enhanced flexibility and real-time adaptability.
+48 510 002 513
+48 601 073 900
contact@noctuai.com
Nowogrodzka 56A, 00-695 Warsaw, Poland
Autonomous data governing and analysis system for all kinds of video data in your organization
