Abstract. In this paper, a basic conceptual architecture aimed at the design of Computer Vision System is qualitatively described. The proposed architecture addresses the design of vision systems in a modular fashion using modules with three distinct units or components: a processing network or diagnostics unit, a control unit and a communications unit. The control of the system at the modules level is designed based on a Discrete Events Model. This basic methodology has been used to design a real-time active vision system for detection, tracking and recognition of people. It is made up of three functional modules aimed at the detection, tracking, recognition of moving individuals plus a supervision module. The detection module is devoted to the detection of moving targets, using optic ow computation and relevant areas extraction. The tracking module uses an adaptive correlation technique to xate on moving objects. The objective of this module is to pursuit the object, centering it into a relocatable focus of attention window ( F OAW) to obtain a good view of the object in order to recognize it. Several focus of attention can betracked simultaneously. The recognition module is designed in an opportunistic style in order to identify the object whenever it is possible. A demonstration system has been developed to detect, track and identify walking people.

Automated surveillance is essential for the protection of Critical Infrastructures (CIs) in future Smart Cities. The dynamic environments and bandwidth requirements demand systems that adapt themselves to react when events of interest occur. We present a reconfigurable Cyber Physical System for the protection of CIs using distributed cloud-edge smart video surveillance. Our local edge nodes perform people detection via Deep Learning. Processing is embedded in high performance SoCs (System-on-Chip) achieving real-time performance (≈ 100 fps-frames per second) which enables efficiently managing video streams of more cameras source at lower frame rate. Cloud server gathers results from nodes to carry out biometric facial identification, tracking, and perimeter monitoring. A Quality and Resource Management module monitors data bandwidth and triggers reconfiguration adapting the transmitted video resolution. This also enables a flexible use of the network by multiple cameras while maintaining the accuracy of biometric identification. A real-world example shows a reduction of ≈ 75% bandwidth use with respect to the no-reconfiguration scenario.

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