A vision-based system for intelligent monitoring: human behaviour analysis and privacy by context

Due to progress and demographic change, society is facing a crucial challenge related to increased life expectancy and a higher number of people in situations of dependency. As a consequence, there exists a significant demand for support systems for personal autonomy. This article outlines the vision@home project, whose goal is to extend independent living at home for elderly and impaired people, providing care and safety services by means of vision-based monitoring. Different kinds of ambient-assisted living services are supported, from the detection of home accidents, to telecare services. In this contribution, the specification of the system is presented, and novel contributions are made regarding human behaviour analysis and privacy protection. By means of a multi-view setup of cameras, people's behaviour is recognised based on human action recognition. For this purpose, a weighted feature fusion scheme is proposed to learn from multiple views. In order to protect the right to privacy of the inhabitants when a remote connection occurs, a privacy-by-context method is proposed. The experimental results of the behaviour recognition method show an outstanding performance, as well as support for multi-view scenarios and real-time execution, which are required in order to provide the proposed services

Design and Implementation of a smart monitoring system of a modern renewable energy micro-grid system using a low-cost data acquisition system and LabVIEWTM program

This paper focuses on designing and implementing a prototype of smart monitoring system capable of doing multi functions i.e. monitoring, analysing and communicating with devices in a small micro-grid system. This research proposes the use of a combination between a low-cost data acquisition (NI compact DAQ) device and a commercially-used LabVIEW™ program in being a smart renewable monitoring and analysing system. The proposed set of equipment is capable of doing all the functions needed by utilities and users in the aforementioned micro-grid system. The proposed prototype is capable of measuring, monitoring and recording in both normal system operating conditions and when faults occurred. A number of power quality parameters that can be monitored and analysed are as follows: voltage (V), current (I), real power (P), reactive power (Q), apparent power (S), energy consumption (kWhr), power factor (PF), harmonics distortion (THDi and THDv), over/under voltage, sag as well as swell. Extensively in this paper, the principle design concepts, hardware implementations, the LabVIEWTM algorithm, how the overall system works, experimental results, the accuracy verification process and the conclusions will be thoroughly elaborated

Renewable Sources and Energy Storage Optimization to Minimize the Global Costs of Railways

Climate change is one of the biggest global issues for humanity these days, and its effect has become more severe. The transport sector accounts for around 30% of greenhouse gas emissions, which need to be decarbonized urgently. Railway electrification is one of the low-carbon solutions, but it still relies on power grids causing carbon emissions. To further decarbonize electric railways, the renewable energy sources (RESs) and energy storage system (ESS) integration scheme for railway traction power network has been proposed. This paper developed the energy management system to calculate the energy flow and global cost. Moreover, contact wire loss and conversion loss were considered. The optimization problem to find the optimal capacity and location of the PV farm, wind farm and energy storage system to achieve the lowest global daily costs was solved by the Brute Force Algorithm. The traction network of the High Speed 2 Railway in the UK has been taken as a case study. Results revealed that the global cost and carbon emissions are reduced considerably with both ESS and RESs installed. In the scenario of the ESS alone, 1.3% of the global cost is saved by capturing the regenerative energy and reusing it. Furthermore, this figure goes up to 10% and 62% when the PV and wind farms are integrated, respectively. When considering all variables, it is found that installing the wind farm is a more economical option than the PV farm. The study also shows that the optimal locations to install the plants and ESS vary by scenario

Respectful Cameras: Detecting Visual Markers in Real-Time to Address Privacy Concerns

Abstract — To address privacy concerns with digital video surveillance cameras, we propose a practical, real-time approach that preserves the ability to observe actions while obscuring individual identities. In our proposed Respectful Cameras system, people who wish to remain anonymous agree to wear colored markers such as a hat or vest. The system automatically tracks these markers using statistical learning and classification to infer the location and size of each face and then inserts elliptical overlays. Our objective is to obscure the face of each individual wearing a marker, while minimizing the overlay area in order to maximize the remaining observable region of the scene. Our approach incorporates a visual color-tracker based on a 9 dimensional color-space by using a Probabilistic AdaBoost classifier with axis-aligned hyperplanes as weak-learners. We then use Particle Filtering to incorporate interframe temporal information. We present experiments illustrating the performance of our system in both indoor and outdoor settings, where occlusions, multiple crossing targets, and lighting changes occur. Results suggest that the Respectful Camera system can reduce false negative rates to acceptable levels (under 2%). I