An essential component of new advanced intelligent video surveillance systems is the possibility to perform non-cooperative people detection and identification. Nowadays Face Detection and Recognition are used in access control systems where the human being is willing to help the system, but the task is much more complex in other unconstrained situations. The paper refers some results of a preliminary study to investigate the most promising approach for face detection in noncooperative conditions with the main objective to reduce as much as possible the number of false alarms working on video-rate processing speed.The proposed solution has been developed around the AdaBoost approach [1], using the open-CV library, with an integration of motion and colour segmentation. The primary scope of the paper is to refer some experimental results to show the potential improvements in terms of reduction of false positives and a significant decrease of the execution time.

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Massive access for Internet-of-Things (IoT) in beyond 5G networks represents a daunting challenge for conventional bandwidth-limited technologies. Millimeter-wave technologies (mmWave)-which provide large chunks of bandwidth at the cost of more complex wireless processors in harsher radio environments-is a promising alternative to accommodate massive IoT but its cost and power requirements are an obstacle for wide adoption in practice. In this context, meta-materials arise as a key innovation enabler to address this challenge by Re-configurable Intelligent Surfaces (RISs).In this paper we take on the challenge and study a beyond 5G scenario consisting of a multi-antenna base station (BS) serving a large set of single-antenna user equipments (UEs) with the aid of RISs to cope with non-line-of-sight paths. Specifically, we build a mathematical framework to jointly optimize the precoding strategy of the BS and the RIS parameters in order to minimize the system sum mean squared error (SMSE). This novel approach reveals convenient properties used to design two algorithms, RISMA and Lo-RISMA, which are able to either find simple and efficient solutions to our problem (the former) or accommodate practical constraints with low-resolution RISs (the latter). Numerical results show that our algorithms outperform conventional benchmarks that do not employ RIS (even with low-resolution meta-surfaces) with gains that span from 20% to 120% in sum rate performance.