Multi-UAV Allocation Framework for predictive crime deterrence and data acquisition

The recent decline in the number of police and security force personnel has raised a serious security issue that could lead to reduced public safety and delayed response to crimes in urban areas. This may be alleviated in part by utilizing micro or small unmanned aerial vehicles (UAVs) and their high-mobility on-board sensors in conjunction with machine-learning techniques such as neural networks to offer better performance in predicting times and places that are high-risk and deterring crimes. The key to the success of such operation lies in the suitable placement of UAVs. This paper proposes a multi-UAV allocation framework for predictive crime deterrence and data acquisition that consists of the overarching methodology, a problem formulation, and an allocation method that work with a prediction model using a machine learning approach. In contrast to previous studies, our framework provides the most effective arrangement of UAVs for maximizing the chance to apprehend offenders whilst also acquiring data that will help improve the performance of subsequent crime prediction. This paper presents the system architecture assumed in this study, followed by a detailed description of the methodology, the formulation of the problem, and the UAV allocation method of the proposed framework. Our framework is tested using a real-world crime dataset to evaluate its performance with respect to the expected number of crimes deterred by the UAV patrol. Furthermore, to address the engineering practice of the proposed framework, we discuss the feasibility of the simulated deployment scenario in terms of energy consumption and the relationship between data analysis and crime prediction

Learning to compare visibility on webcam images

International audienceFrom the beginning of the 2000’s, cameras are considered as an interesting source of opportunistic meteorological data. This short study deals with the comparison of meteorological visibility between images.A new dataset has been built from publicly available webcam sequences. An original labeling process, based on a mergesort algorithm, allowed us to sort more than 400 webcam sequences with respect to the meteorological visibility. Standard CNN have been trained on these sequences in a basic “learning to compare” framework and tested on independent webcams that are colocalized with visibilimeters. Results on the comparison task are promising. We observe that taking into account the numerous abstention cases improves our predictions

Briefing: UK-RAS white paper in robotics and autonomous systems for resilient infrastructure

This paper presents an extended briefing of the recently published UK-Robotics and autonomous systems (RAS) network Whie Paper in RAS for resilient infrastructure. It aims at setting out a vision of what RAS systems will be able to deliver in infrastructure, what are the current barriers and challenges to achieve that vision and what can the UK Government do to ensure that the UK remains at the forefront in this field