Surv-AI-llance

An order-of-magnitude leap over the state of the art

The Surv-AI-llance project will develop an innovative, multimodal computer vision solution that relies on skeleton-based representation and Doppler radar. This technology enables algorithms to observe human bodies as structures moving through time and space in order to accurately interpret human actions. The solution offers significant efficiency and accuracy improvements and runs using edge intelligence, which enables distributed devices to process and analyze data locally by only exchanging high-level information.

Fast, efficient and privacy oriented

The overarching objective of the Surv-AI-llance consortium is to build a reliable, privacy-friendly video analytics pipeline that accurately interprets scenes under surveillance and rapidly alerts officials. The research goals of the project include:

• developing computer vision methods that use many camera and sensor devices that analyze human actions and evolving scenes over time – in a wide range of conditions;
• creating methods for distributed computer vision model training using edge computing;
• innovating an integrated, reconfigurable system that offers new human behavior analysis capabilities and provides needed visual information to officials.

Truly smart surveillance

The project will result in valuable leaps of knowledge in the areas of vision-based abnormal human activity recognition, radar-based human activity recognition, data fusion, distributed AI and explainable artificial intelligence (XAI). Research outcomes will support the development of safety monitoring solutions and the smart cities movement, alleviating the burdens of human safety officers and unlocking real-time incident response rates.

“The Surv-AI-llance project aims to gather reliable and actionable insights into human behavior by developing advanced machine-learning algorithms based on data from cameras and radar devices.”