Instead of relying on wide-scale blurring in video footage,
Synthetic Anonymization removes sensitive biometric data
while preserving valuable analytical metrics to enable model training and unlock video analytics potential with real-time,
Syntonym not only anonymizes faces in visual data but also creates synthetic overlays for license plates, to address privacy concerns without damaging the structure of the data.
As in-cabin transforms into more of a personal space, being constantly recorded inside the vehicle, even for the sake of safety, is holding consumers back.
Synthetic Anonymization is a privacy enabler for mobility tech developers in collecting real-time insights as to the awareness, engagement, responsiveness and availability of the driver.
In all cases and driving situations, every data subject inside the vehicle will be able to decide upon how and when they're monitored.
With real-time lossless anonymization, in-cabin intelligence gathered through safety assurance systems will not be affected by varying privacy preferences.
Addressing privacy concerns towards video surveillance in public spaces, Synthetic Anonymization provides lossless, high-quality visual data in real-time for the sustainable development of smart transport and smart city technologies.
Syntonym provides lossless visual data set that is diversified and enriched, according to requirements of specific training/test scenarios, by using Syntonym’s training data set consisting of non-existent faces.
Going beyond traditional techniques such as blurring, Syntonym provides lossless visual data in real-time, by preserving anonymized analytical metrics (head pose, facial expressions and eye movements) that are crucial for the accuracy and reliability of AD and ADAS algorithms.