cREAtIve

Deep learning applications without the cloud

Deep learning solutions for object classification rely mostly on cloud-based data processing which has sufficient power, bandwidth and storage to operate. 

Camera-based systems can also use deep learning algorithms to detect, recognize and track objects, but these systems need zero latency and are impacted by privacy concerns. Since they cannot rely on the processing power of the cloud, these systems must be equipped with embedded hardware that is able to perform efficient and robust deep learning algorithms in real-time. 

Measurements provided by position sensors are corrupted by non-stationary noise caused by varying environmental conditions. Novel lightweight noise canceling solutions capable to cope with the non-stationary nature of the perturbations and operating directly on the sensing devices are needed in order to increase the accuracy and robustness of the sensor measurements.

LIDAR (laser-based detection) systems are essential in advanced driver-assistance systems and autonomous driving. Innovative embedded deep learning solutions are required in order to improve object detection, tracking and classification accuracy for such devices. 

Adaptable algorithms

In addition to coping with power, bandwidth and processing constraints, a camera-based deep learning solution for traffic management would also need to adapt to different environments and weather conditions. The cREAtIve consortium will develop an original deep learning solution with the required adaptability, as well as the underlying hardware needed to optimally support it. 

Embedded devices become intelligent

cREAtIve consortium partners are experts in thermal cameras and traffic monitoring solutions, automotive sensors, LIDAR systems, deep learning and adaptable hardware solutions. Together, they will address the following innovation goals:

• Develop AI sensors that filter out noise caused by temperature changes and stray magnetic fields;
• Improve the processing of LIDAR (laser-based detection) measurements to boost 3D object detection and tracking performance;
• Design a new deep-learning paradigm that enhances the accuracy of object detection and classification in thermal images;
• Investigate hardware trade-offs for deep networks in embedded and edge systems where resources and power consumption are constrained;
• Empower the embedded deep network to adjust itself to different environments.

Improved traffic safety and more

The outcomes of the cREAtIve project will be key advancements in the development of competitive, adaptive AI solutions in resource-limited embedded devices. In practice, the results of the project could be used to provide stable measurements in position sensing devices in automotive industry, better vehicle detection and tracking in autonomous driving, and improved traffic management and pedestrian counting.

“The cREAtIve project will develop novel, adaptive and robust deep learning solutions for resource-limited embedded devices.”