Learning from Multiple Sources for Video Summarisation

Many visual surveillance tasks, e.g.video summarisation, is conventionally accomplished through analysing imagerybased features. Relying solely on visual cues for public surveillance video understanding is unreliable, since visual observations obtained from public space CCTV video data are often not sufficiently trustworthy and events of interest can be subtle. On the other hand, non-visual data sources such as weather reports and traffic sensory signals are readily accessible but are not explored jointly to complement visual data for video content analysis and summarisation. In this paper, we present a novel unsupervised framework to learn jointly from both visual and independently-drawn non-visual data sources for discovering meaningful latent structure of surveillance video data. In particular, we investigate ways to cope with discrepant dimension and representation whist associating these heterogeneous data sources, and derive effective mechanism to tolerate with missing and incomplete data from different sources. We show that the proposed multi-source learning framework not only achieves better video content clustering than state-of-the-art methods, but also is capable of accurately inferring missing non-visual semantics from previously unseen videos. In addition, a comprehensive user study is conducted to validate the quality of video summarisation generated using the proposed multi-source model

A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain

Analysis of Illegal Parking Behavior in Lisbon: Predicting and Analyzing Illegal Parking Incidents in Lisbon´s Top 10 Critical Streets

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIllegal parking represents a costly and pervasive problem for most cities, as it not only leads to an increase in traffic congestion and the emission of air pollutants but also compromises pedestrian, biking, and driving safety. Moreover, it obstructs the flow of emergency vehicles, delivery services, and other essential functions, posing a significant risk to public safety and impeding the efficient operation of urban services. These detrimental effects ultimately diminish the cleanliness, security, and overall attractiveness of cities, impacting the well-being of both residents and visitors alike. Traditionally, decision-support systems utilized for addressing illegal parking have heavily relied on costly camera systems and complex video-processing algorithms to detect and monitor infractions in real time. However, the implementation of such systems is often challenging and expensive, particularly considering the diverse and dynamic road environment conditions. Alternatively, research studies focusing on spatiotemporal features for predicting parking infractions present a more efficient and cost-effective approach. This project focuses on the development of a machine learning model to accurately predict illegal parking incidents in the ten highly critical streets of Lisbon Municipality, taking into account the hour period and whether it is a weekend or holiday. A comprehensive evaluation of various machine learning algorithms was conducted, and the k-nearest neighbors (KNN) algorithm emerged as the top performing model. The KNN model exhibited robust predictive capabilities, effectively estimating the occurrence of illegal parking in the most critical streets, and together with the creation of an interactive and user-friendly dashboard, this project contributes valuable insights for urban planners, policymakers, and law enforcement agencies, empowering them to enhance public safety and security through informed decision-making

Advanced Topics in Systems Safety and Security

This book presents valuable research results in the challenging field of systems (cyber)security. It is a reprint of the Information (MDPI, Basel) - Special Issue (SI) on Advanced Topics in Systems Safety and Security. The competitive review process of MDPI journals guarantees the quality of the presented concepts and results. The SI comprises high-quality papers focused on cutting-edge research topics in cybersecurity of computer networks and industrial control systems. The contributions presented in this book are mainly the extended versions of selected papers presented at the 7th and the 8th editions of the International Workshop on Systems Safety and Security—IWSSS. These two editions took place in Romania in 2019 and respectively in 2020. In addition to the selected papers from IWSSS, the special issue includes other valuable and relevant contributions. The papers included in this reprint discuss various subjects ranging from cyberattack or criminal activities detection, evaluation of the attacker skills, modeling of the cyber-attacks, and mobile application security evaluation. Given this diversity of topics and the scientific level of papers, we consider this book a valuable reference for researchers in the security and safety of systems

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Background Subtraction with Dirichlet Process Mixture Models

ESPRC Grant: EP/G063974/

Wrong Way Vehicle Detection in Single and Double Lane

Wrong-way driving is one of the primary causes of traffic jams and accidents globally. It is possible to identify vehicles going the wrong direction, which lessens accidents and traffic congestion. Surveillance footage has become an important source of data due to the accessibility of less priced cameras and the expanding use of real-time traffic management systems. In this paper, we propose a technique for automatically identifying automobiles moving against traffic. Our system uses the You Only Look Once (CNN) algorithm to recognize and track vehicles from video inputs and the centroid tracking method to determine each vehicle's orientation inside a given region of interest (ROI) in order to identify vehicles traveling in the wrong direction. It functions in three steps. The Deep sort tracking method is particularly good in detecting and tracking objects, and the centroid tracking technique can effectively monitor the direction of travel. Experiments with a variety of traffic films show that the suggested method can detect and identify wrong-way moving vehicles in a variety of lighting and weather scenarios. The interface of the system is quite simple and easy to use