We propose an extension of a neural-based background subtraction approach to moving object detection to the case of image sequences taken from pan-tilt-zoom (PTZ) cameras. The background model automatically adapts in a self-organizing way to changes in the scene background. Background variations arising in a usual stationary camera setting, such as those due to gradual illumination changes, to waving trees, or to shadows cast by moving objects, are accurately handled by the neural self-organizing background model originally proposed for this type of setting. Handling of variations due to the PTZ camera movement is ensured by a novel registration mechanism that allows the neural background model to automatically compensate the eventual ego-motion, estimated at each time instant. Experimental results on several real image sequences and comparisons with seven state-of-the-art methods demonstrate the accuracy of the proposed approach.

Blockchain technology has been applied in a wide range of domains and has seen major developments in the last years, notably when integrated with other emerging technologies. In this paper, we focus on the integration of blockchain and digital twins with the aim of enhancing the capabilities of both technologies. In particular, we demonstrate how blockchain can improve critical aspects of the security and transparency of digital twins by analyzing a real-world scenario and evaluating produced experimental data. This research identifies and addresses critical vulnerabilities in digital twins, particularly data integrity and transparency, through blockchain-based validation mechanisms and smart-contract integration. Various blockchain-related and digital twin-related technologies are employed to enable the repeatability of the suggested approach. Additionally, an in-depth analysis of such integration is provided to facilitate a symbiotic relationship between these technologies by addressing key challenges, such as scalability, interoperability, and performance, along with viable solutions that could advance their co-evolution in both academic research and industrial applications.