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The problem of performing remote biomedical measurements using just a video stream of a subject face is called remote photoplethysmography (rPPG). The aim of this work is to propose a novel method able to perform rPPG using single-photon avalanche diode (SPAD) cameras. These are extremely accurate cameras able to detect even a single photon and are already used in many other applications. Moreover, a novel method that mixes deep learning and traditional signal analysis is proposed in order to extract and study the pulse signal. Experimental results show that this system achieves accurate results in the estimation of biomedical information such as heart rate, respiration rate, and tachogram. Lastly, thanks to the adoption of the deep learning segmentation method and dependability checks, this method could be adopted in non-ideal working conditions—for example, in the presence of partial facial occlusions.

Trajectory estimation and 3D scene reconstruction from multiple cameras (also referred as Structure from Motion, SfM) will have a central role in the future of automotive industry. Typical appliance fields will be: autonomous navigation/guidance, collisions avoidance against static or moving objects (in particular pedestrians), parking assisted maneuvers and many more. The work exposed in this paper had mainly two different goals: (1) to describe the implementation of a real time embedded SfM modular pipeline featuring a dedicated optimized HW/SW system partitioning. It included also nonlinear optimizations such as local and global bundle adjustment at different stages of the pipeline; (2) to demonstrate quantitatively its performances on a synthetic test space specifically designed for its characterization. In order to make the system reliable and effective, providing the driver or the autonomous vehicle with a prompt response, the data rates and low latency of the 5G communication systems appear to make this choice the most promising communication solution

Hundreds of millions of images are uploaded to the cloud every day. Innovative applications able to analyze and extract efficiently information from such a big database are needed nowadays more than ever. Visual search is an application able to retrieve information of a query image comparing it against a large image database. In this paper a Visual Search pipeline implementation is presented able to retrieve multiple objects depicted in a single query image. Quantitative and qualitative precision results are shown on both real and synthetic datasets