The crowd as a cameraman : on-stage display of crowdsourced mobile video at large-scale events

Recording videos with smartphones at large-scale events such as concerts and festivals is very common nowadays. These videos register the atmosphere of the event as it is experienced by the crowd and offer a perspective that is hard to capture by the professional cameras installed throughout the venue. In this article, we present a framework to collect videos from smartphones in the public and blend these into a mosaic that can be readily mixed with professional camera footage and shown on displays during the event. The video upload is prioritized by matching requests of the event director with video metadata, while taking into account the available wireless network capacity. The proposed framework's main novelty is its scalability, supporting the real-time transmission, processing and display of videos recorded by hundreds of simultaneous users in ultra-dense Wi-Fi environments, as well as its proven integration in commercial production environments. The framework has been extensively validated in a controlled lab setting with up to 1 000 clients as well as in a field trial where 1 183 videos were collected from 135 participants recruited from an audience of 8 050 people. 90 % of those videos were uploaded within 6.8 minutes

DESIGN FRAMEWORK FOR INTERNET OF THINGS BASED NEXT GENERATION VIDEO SURVEILLANCE

Modern artificial intelligence and machine learning opens up new era towards video surveillance system. Next generation video surveillance in Internet of Things (IoT) environment is an emerging research area because of high bandwidth, big-data generation, resource constraint video surveillance node, high energy consumption for real time applications. In this thesis, various opportunities and functional requirements that next generation video surveillance system should achieve with the power of video analytics, artificial intelligence and machine learning are discussed. This thesis also proposes a new video surveillance system architecture introducing fog computing towards IoT based system and contributes the facilities and benefits of proposed system which can meet the forthcoming requirements of surveillance. Different challenges and issues faced for video surveillance in IoT environment and evaluate fog-cloud integrated architecture to penetrate and eliminate those issues. The focus of this thesis is to evaluate the IoT based video surveillance system. To this end, two case studies were performed to penetrate values towards energy and bandwidth efficient video surveillance system. In one case study, an IoT-based power efficient color frame transmission and generation algorithm for video surveillance application is presented. The conventional way is to transmit all R, G and B components of all frames. Using proposed technique, instead of sending all components, first one color frame is sent followed by a series of gray-scale frames. After a certain number of gray-scale frames, another color frame is sent followed by the same number of gray-scale frames. This process is repeated for video surveillance system. In the decoder, color information is formulated from the color frame and then used to colorize the gray-scale frames. In another case study, a bandwidth efficient and low complexity frame reproduction technique that is also applicable in IoT based video surveillance application is presented. Using the second technique, only the pixel intensity that differs heavily comparing to previous frame’s corresponding pixel is sent. If the pixel intensity is similar or near similar comparing to the previous frame, the information is not transferred. With this objective, the bit stream is created for every frame with a predefined protocol. In cloud side, the frame information can be reproduced by implementing the reverse protocol from the bit stream. Experimental results of the two case studies show that the IoT-based proposed approach gives better results than traditional techniques in terms of both energy efficiency and quality of the video, and therefore, can enable sensor nodes in IoT to perform more operations with energy constraints

Video Forensics in Cloud Computing: The Challenges & Recommendations

Forensic analysis of large video surveillance datasets requires computationally demanding processing and significant storage space. The current standalone and often dedicated computing infrastructure used for the purpose is rather limited due to practical limits of hardware scalability and the associated cost. Recently Cloud Computing has emerged as a viable solution to computing resource limitations, taking full advantage of virtualisation capabilities and distributed computing technologies. Consequently the opportunities provided by cloud computing service to support the requirements of forensic video surveillance systems have been recently studied in literature. However such studies have been limited to very simple video analytic tasks carried out within a cloud based architecture. The requirements of a larger scale video forensic system are significantly more and demand an in-depth study. Especially there is a need to balance the benefits of cloud computing with the potential risks of security and privacy breaches of the video data. Understanding different legal issues involved in deploying video surveillance in cloud computing will help making the proposed security architecture affective against potential threats and hence lawful. In this work we conduct a literature review to understand the current regulations and guidelines behind establishing a trustworthy, cloud based video surveillance system. In particular we discuss the requirements of a legally acceptable video forensic system, study the current security and privacy challenges of cloud based computing systems and make recommendations for the design of a cloud based video forensic system

Video forensics in cloud computing: the challenges & recommendations

Forensic analysis of large video surveillance datasets requires computationally demanding processing and significant storage space. The current standalone and often dedicated computing infrastructure used for the purpose is rather limited due to practical limits of hardware scalability and the associated cost. Recently Cloud Computing has emerged as a viable solution to computing resource limitations, taking full advantage of virtualisation capabilities and distributed computing technologies. Consequently the opportunities provided by cloud computing service to support the requirements of forensic video surveillance systems have been recently studied in literature. However such studies have been limited to very simple video analytic tasks carried out within a cloud based architecture. The requirements of a larger scale video forensic system are significantly more and demand an in-depth study. Especially there is a need to balance the benefits of cloud computing with the potential risks of security and privacy breaches of the video data. Understanding different legal issues involved in deploying video surveillance in cloud computing will help making the proposed security architecture affective against potential threats and hence lawful. In this work we conduct a literature review to understand the current regulations and guidelines behind establishing a trustworthy, cloud based video surveillance system. In particular we discuss the requirements of a legally acceptable video forensic system, study the current security and privacy challenges of cloud based computing systems and make recommendations for the design of a cloud based video forensic system

Fog computing enabled cost-effective distributed summarization of surveillance videos for smart cities

[EN] Fog computing is emerging an attractive paradigm for both academics and industry alike. Fog computing holds potential for new breeds of services and user experience. However, Fog computing is still nascent and requires strong groundwork to adopt as practically feasible, cost-effective, efficient and easily deployable alternate to currently ubiquitous cloud. Fog computing promises to introduce cloud-like services on local network while reducing the cost. In this paper, we present a novel resource efficient framework for distributed video summarization over a multi-region fog computing paradigm. The nodes of the Fog network is based on resource constrained device Raspberry Pi. Surveillance videos are distributed on different nodes and a summary is generated over the Fog network, which is periodically pushed to the cloud to reduce bandwidth consumption. Different realistic workload in the form of a surveillance videos are used to evaluate the proposed system. Experimental results suggest that even by using an extremely limited resource, single board computer, the proposed framework has very little overhead with good scalability over off-the-shelf costly cloud solutions, validating its effectiveness for IoT-assisted smart cities. (C) 2018 Elsevier Inc. All rights reserved.Nasir, M.; Muhammad, K.; Lloret, J.; Sangaiah, AK.; Sajjad, M. (2019). Fog computing enabled cost-effective distributed summarization of surveillance videos for smart cities. Journal of Parallel and Distributed Computing. 126:161-170. https://doi.org/10.1016/j.jpdc.2018.11.004S16117012

CCTV Surveillance System, Attacks and Design Goals

Closed Circuit Tele-Vision surveillance systems are frequently the subject of debate. Some parties seek to promote their benefits such as their use in criminal investigations and providing a feeling of safety to the public. They have also been on the receiving end of bad press when some consider intrusiveness has outweighed the benefits. The correct design and use of such systems is paramount to ensure a CCTV surveillance system meets the needs of the user, provides a tangible benefit and provides safety and security for the wider law-abiding public. In focusing on the normative aspects of CCTV, the paper raises questions concerning the efficiency of understanding contemporary forms of ‘social ordering practices’ primarily in terms of technical rationalities while neglecting other, more material and ideological processes involved in the construction of social order. In this paper, a 360-degree view presented on the assessment of the diverse CCTV video surveillance systems (VSS) of recent past and present in accordance with technology. Further, an attempt been made to compare different VSS with their operational strengths and their attacks. Finally, the paper concludes with a number of future research directions in the design and implementation of VSS

Análisis de video y clasificación de objetos en una plataforma de vigilancia de código abierto utilizando procesamiento diferido

La inseguridad es un problema que afecta a todas las ciudades del mundo. Las más informatizadas recurren a la videovigilancia para combatirla, montando centros de monitoreo con cientos de cámaras y personal idóneo que realiza las tareas de observación. Sin embargo, este método parece no ser suficiente y los organismos públicos deben atender un reclamo social por mayor transparencia y eficiencia en el accionar ante un delito. Aunque existen soluciones corporativas, estas tienden a centralizar el desarrollo y no es sencillo añadir nuevas funcionalidades. En este contexto es que surge el presente proyecto: una plataforma de administración de cámaras y sensores basada en bibliotecas de código abierto, para apoyar la gestión integral de la seguridad. Esta plataforma complementa técnicas básicas de análisis automatizado de videos, tal como la detección de movimiento y seguimiento de objetos, hasta otras específicas, como la clasificación de objetos o la identificación de patentes. La arquitectura desarrollada es abierta, escalable y permite añadir diferentes tipos de algoritmos a partir de un pipeline de análisis diferido. En este trabajo se presenta la estructura ideada, los algoritmos actualmente utilizados para el seguimiento y clasificación de objetos, y diferentes resultados de análisis y eficiencia de la propuesta.Trabajo presentado por el Laboratorio de Plasmas Densos (PLADEMA

Development and evaluation of a smartphone-based system for inspection of road maintenance work

Abstract. In the road construction industry, doing work inspection is a laborious and resource-consuming job because of the distributed work site. Contractors in Finland require to capture photos of every road fix they have done as proof of their work. It is well-established that with the help of smartphone technology, these kinds of manual work can be reduced. This thesis aims to develop and evaluate a smartphone-based system to capture video evidence of task completion. The system, designed and developed in this thesis, consists of an Android application named ’Road Recorder’ and a web tool for managing the content collected by Road Recorder. While mounted to a vehicle’s dashboard used in construction work, the Road Recorder can record the videos of road surface and geo-location information and some other metadata and send them to a remote server that is inspected using the web tool. Users of different backgrounds were given the system to accomplish some tasks and were observed closely. The users were interviewed at the end, and responses were analyzed to find the usability of the applications. The results indicate the high usability of the Road Recorder application and reveal possible improvements for the Road Recorder management web application. Overall, Road Recorder is a great step towards the automation of such construction work inspection. Though there were some limitations in the evaluation process, it demonstrates that Road Recorder is easy to use and can be a useful tool in the industry

Vacant Parking Lot Information System Using Transfer Learning and IoT

Parking information systems have become very important, especially in metropolitan areas as they help to save time, effort and fuel when searching for parking. This paper offers a novel low-cost deep learning approach to easily implement vacancy detection at outdoor parking spaces with CCTV surveillance. The proposed method also addresses issues due to perspective distortion in CCTV images. The architecture consists of three classifiers for checking the availability of parking spaces. They were developed on the TensorFlow platform by re-training MobileNet (a pre-trained Convolutional Neural Network (CNN)) model using the transfer learning technique. A performance analysis showed 88% accuracy for vacancy detection. An end-to-end application model with Internet of Things (IoT) and an Android application is also presented. Users can interact with the cloud using their Android application to get real-time updates on parking space availability and the parking location. In the future, an autonomous car could use this system as a V2I (Vehicle to Infrastructure) application in deciding the nearest parking space