2024
Multi-Camera Anonymization at the Edge for Open Road Tests
Instantly anonymize faces, license plates, and sensitive data across multiple cameras during open road tests - preserving critical insights with zero latency.
Syntonym enables simultaneous, real-time anonymization across multiple cameras deployed in vehicles during open road tests. Sensitive data such as faces, license plates, and other identifiers are instantly replaced or blurred, preserving vital behavioral and environmental information while ensuring full compliance and minimal latency.
Realtime
External Multi Camera
Faces/LP's
Instantly anonymize faces, license plates, and sensitive data across multiple cameras during open road tests - preserving critical insights with zero latency.
Syntonym enables simultaneous, real-time anonymization across multiple cameras deployed in vehicles during open road tests. Sensitive data such as faces, license plates, and other identifiers are instantly replaced or blurred, preserving vital behavioral and environmental information while ensuring full compliance and minimal latency.
Data from external cameras (ADAS, survey vehicles) captures pedestrians, other cars, and license plates, creating a massive compliance liability
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Problem
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Solution
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Realtime-Edge
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
More Works
(GQ® — 02)
©2024
FAQ
01
What does Syntonym do?
02
What is "Lossless Anonymization"?
03
How is this different from just blurring?
04
When should I choose Syntonym Lossless vs. Syntonym Blur?
05
What are the deployment options (Cloud API, Private Cloud, SDK)?
06
Can the anonymization be reversed?
07
Is Syntonym compliant with regulations like GDPR and CCPA?
08
How do you ensure the security of our data with the Cloud API?
2024
Multi-Camera Anonymization at the Edge for Open Road Tests
Instantly anonymize faces, license plates, and sensitive data across multiple cameras during open road tests - preserving critical insights with zero latency.
Syntonym enables simultaneous, real-time anonymization across multiple cameras deployed in vehicles during open road tests. Sensitive data such as faces, license plates, and other identifiers are instantly replaced or blurred, preserving vital behavioral and environmental information while ensuring full compliance and minimal latency.
Realtime
External Multi Camera
Faces/LP's
Instantly anonymize faces, license plates, and sensitive data across multiple cameras during open road tests - preserving critical insights with zero latency.
Syntonym enables simultaneous, real-time anonymization across multiple cameras deployed in vehicles during open road tests. Sensitive data such as faces, license plates, and other identifiers are instantly replaced or blurred, preserving vital behavioral and environmental information while ensuring full compliance and minimal latency.
Data from external cameras (ADAS, survey vehicles) captures pedestrians, other cars, and license plates, creating a massive compliance liability
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Problem
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Solution
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Realtime-Edge
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
More Works
(GQ® — 02)
©2024
FAQ
01
What does Syntonym do?
02
What is "Lossless Anonymization"?
03
How is this different from just blurring?
04
When should I choose Syntonym Lossless vs. Syntonym Blur?
05
What are the deployment options (Cloud API, Private Cloud, SDK)?
06
Can the anonymization be reversed?
07
Is Syntonym compliant with regulations like GDPR and CCPA?
08
How do you ensure the security of our data with the Cloud API?
2024
Multi-Camera Anonymization at the Edge for Open Road Tests
Instantly anonymize faces, license plates, and sensitive data across multiple cameras during open road tests - preserving critical insights with zero latency.
Syntonym enables simultaneous, real-time anonymization across multiple cameras deployed in vehicles during open road tests. Sensitive data such as faces, license plates, and other identifiers are instantly replaced or blurred, preserving vital behavioral and environmental information while ensuring full compliance and minimal latency.
Realtime
External Multi Camera
Faces/LP's
Instantly anonymize faces, license plates, and sensitive data across multiple cameras during open road tests - preserving critical insights with zero latency.
Syntonym enables simultaneous, real-time anonymization across multiple cameras deployed in vehicles during open road tests. Sensitive data such as faces, license plates, and other identifiers are instantly replaced or blurred, preserving vital behavioral and environmental information while ensuring full compliance and minimal latency.
Data from external cameras (ADAS, survey vehicles) captures pedestrians, other cars, and license plates, creating a massive compliance liability
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Problem
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Solution
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Realtime-Edge
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII) Anonymize pedestrians' faces and other vehicles' license plates on the fly. Collect vast amounts of real-world data to train robust perception models for autonomous driving without the risk of storing personally identifiable information (PII)
More Works
©2024
FAQ
What does Syntonym do?
What is "Lossless Anonymization"?
How is this different from just blurring?
When should I choose Syntonym Lossless vs. Syntonym Blur?
What are the deployment options (Cloud API, Private Cloud, SDK)?
Can the anonymization be reversed?
Is Syntonym compliant with regulations like GDPR and CCPA?
How do you ensure the security of our data with the Cloud API?