Interactive searching and browsing of video archives: using text and using image matching

Over the last number of decades much research work has been done in the general area of video and audio analysis. Initially the applications driving this included capturing video in digital form and then being able to store, transmit and render it, which involved a large effort to develop compression and encoding standards. The technology needed to do all this is now easily available and cheap, with applications of digital video processing now commonplace, ranging from CCTV (Closed Circuit TV) for security, to home capture of broadcast TV on home DVRs for personal viewing. One consequence of the development in technology for creating, storing and distributing digital video is that there has been a huge increase in the volume of digital video, and this in turn has created a need for techniques to allow effective management of this video, and by that we mean content management. In the BBC, for example, the archives department receives approximately 500,000 queries per year and has over 350,000 hours of content in its library. Having huge archives of video information is hardly any benefit if we have no effective means of being able to locate video clips which are of relevance to whatever our information needs may be. In this chapter we report our work on developing two specific retrieval and browsing tools for digital video information. Both of these are based on an analysis of the captured video for the purpose of automatically structuring into shots or higher level semantic units like TV news stories. Some also include analysis of the video for the automatic detection of features such as the presence or absence of faces. Both include some elements of searching, where a user specifies a query or information need, and browsing, where a user is allowed to browse through sets of retrieved video shots. We support the presentation of these tools with illustrations of actual video retrieval systems developed and working on hundreds of hours of video content

InSPeCT: Integrated Surveillance for Port Container Traffic

This paper describes a fully-operational content-indexing and management system, designed for monitoring and profiling freight-based vehicular traffic in a seaport environment. The 'InSPeCT' system captures video footage of passing vehicles and uses tailored OCR to index the footage according to vehicle license plates and freight codes. In addition to real-time functionality such as alerting, the system provides advanced search techniques for the efficient retrieval of records, where each vehicle is profiled according to multi-angled video, context information, and links to external information sources. Currently being piloted at a busy national seaport, the feedback from port officials indicates the system to be extremely useful in supplementing their existing transportation-security structures

Electronic surveillance in hospitals: A review

This paper focuses on the increasing use of electronic surveillance systems in hospitals and the apparent lack of awareness of the implications of these systems for privacy of the individual. The systems are used for identification and tracking of equipment, staff and patients. There has been little public comment or analysis of these systems with regard to privacy as their implementation has been driven by security issues. The systems that gather this information include video, smart card and more recently RFID systems. The system applications include tracking of vital equipment, labelling of blood and other samples, tracking of patients, new born babies and staff. These applications generate a vast amount of digital information that needs to be correctly secured to protect the privacy of the individual. Separately each type of information has value, but if this information were analysed together then the intelligence that can be gleaned from this could become a major threat to privacy and security. There are various standards and legislation that cover healthcare information, such as CCTV, but are these known and what are the compliance levels? RFID use is increasing in the hospital sector and this is being linked with the patient medical record as it is becoming core to treatment in some hospitals. The indications are that this will become normal practice which means that surveillance information from RFID systems will be linked much more closely to a patient’s medical record. Managers, owners and custodians of information within hospitals need to be aware of the issues and take steps to ensure that staff are fully aware and trained in information handling practices. They also need to ensure that external parties who handle surveillance information are compliant with standards and good practice

Spatio-temporal Texture Modelling for Real-time Crowd Anomaly Detection

With the rapidly increasing demands from surveillance and security industries, crowd behaviour analysis has become one of the hotly pursued video event detection frontiers within the computer vision arena in recent years. This research has investigated innovative crowd behaviour detection approaches based on statistical crowd features extracted from video footages. In this paper, a new crowd video anomaly detection algorithm has been developed based on analysing the extracted spatio-temporal textures. The algorithm has been designed for real-time applications by deploying low-level statistical features and alleviating complicated machine learning and recognition processes. In the experiments, the system has been proven a valid solution for detecting anomaly behaviours without strong assumptions on the nature of crowds, for example, subjects and density. The developed prototype shows improved adaptability and efficiency against chosen benchmark systems

Crowd anomaly detection for automated video surveillance

Video-based crowd behaviour detection aims at tackling challenging problems such as automating and identifying changing crowd behaviours under complex real life situations. In this paper, real-time crowd anomaly detection algorithms have been investigated. Based on the spatio-temporal video volume concept, an innovative spatio-temporal texture model has been proposed in this research for its rich crowd pattern characteristics. Through extracting and integrating those crowd textures from surveillance recordings, a redundancy wavelet transformation-based feature space can be deployed for behavioural template matching. Experiment shows that the abnormality appearing in crowd scenes can be identified in a real-time fashion by the devised method. This new approach is envisaged to facilitate a wide spectrum of crowd analysis applications through automating current Closed-Circuit Television (CCTV)-based surveillance systems

Pathologic Myopia: Complications and Visual Rehabilitation

High myopia, defined as refractive error of at least −6.00D or an axial length of 26.5 mm or more, can induce many modifications in eye’s anatomy that can lead to complications. When high myopia is able to decrease best corrected visual acuity (BCVA) due to its complications, it is called pathologic myopia. Pathologic myopia is one of the major causes of blindness, and it represents a serious issue, since incidence of myopia and high myopia is constantly rising. For educational purposes, in this chapter, complications of pathologic myopia will be divided into anterior (when structures external to the globe or anterior to the ora serrata are involved, such as motility disturbances and cataract) and posterior (when structures posterior to the ora serrata are involved, such as lacquer cracks, chorioretinal atrophy, Fuchs maculopathy, myopic choroidal neovascularization, and retinal detachment). Many treatments are available for pathologic myopia complications depending on their type, such as vascular endothelial growth factor (anti-VEGF) injections and surgery. We will focus on visual rehabilitation interventions, such as visual biofeedback and visual aids that in many cases are the only chance that the ophthalmologist has in order to help patients suffering from pathologic myopia to use at their maximum their residual vision

Detection and Localization of Root Damages in Underground Sewer Systems using Deep Neural Networks and Computer Vision Techniques

Indiana University-Purdue University Indianapolis (IUPUI)The maintenance of a healthy sewer infrastructure is a major challenge due to the root damages from nearby plants that grow through pipe cracks or loose joints, which may lead to serious pipe blockages and collapse. Traditional inspections based on video surveillance to identify and localize root damages within such complex sewer networks are inefficient, laborious, and error-prone. Therefore, this study aims to develop a robust and efficient approach to automatically detect root damages and localize their circumferential and longitudinal positions in CCTV inspection videos by applying deep neural networks and computer vision techniques. With twenty inspection videos collected from various resources, keyframes were extracted from each video according to the difference in a LUV color space with certain selections of local maxima. To recognize distance information from video subtitles, OCR models such as Tesseract and CRNN-CTC were implemented and led to a 90% of recognition accuracy. In addition, a pre-trained segmentation model was applied to detect root damages, but it also found many false positive predictions. By applying a well-tuned YoloV3 model on the detection of pipe joints leveraging the Convex Hull Overlap (CHO) feature, we were able to achieve a 20% improvement on the reliability and accuracy of damage identifications. Moreover, an end-to-end deep learning pipeline that involved Triangle Similarity Theorem (TST) was successfully designed to predict the longitudinal position of each identified root damage. The prediction error was less than 1.0 feet

Electronic Number Plate Generation for Performance Evaluation

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/ICCST52959.2022.9896515The authors have been involved in real world analysis of Automatic Number Plate Recognition (ANPR) data and systems particularly for law enforcement applications. As a result of such work with Law Enforcement Agencies, contributions have been made to the revision of the British Standards for ANPR. This led to the research team developing performance evaluation measures from an end-to-end system perspective. One such measure was the generation of synthetic image datasets suitable for ANPR performance evaluation. The prime requirement for any ANPR system is data accuracy. This paper reports the initial work and progress made using defined synthetic images to test and assess ANPR engines using a structured methodology

Sports Analytics With Computer Vision

Computer vision in sports analytics is a relatively new development. With multi-million dollar systems like STATS’s SportVu, professional basketball teams are able to collect extremely fine-detailed data better than ever before. This concept can be scaled down to provide similar statistics collection to college and high school basketball teams. Here we investigate the creation of such a system using open-source technologies and less expensive hardware. In addition, using a similar technology, we examine basketball free throws to see whether a shooter’s form has a specific relationship to a shot’s outcome. A system that learns this relationship could be used to provide feedback on a player’s shooting form

RFID based Anti-theft System for Metropolia UAS Electronics laboratories

The aim of this thesis is to study different types of RFID based anti-Theft system implementation suitable for Metropolia Electronics laboratory environment to deter theft taking into account several installation requirements. The operating frequencies of the RFID anti-theft system are from low frequency to High frequencies range and governed by different standards based on the region it is going to be implemented. The introduction of this thesis will go through Radio Frequency Identification (RFID) and different RFID based anti-theft system advantages in various areas for instance in access management and control application. Study current Metropolia UAS electronics laboratory overall control mechanism comparing to the anti-theft RFID system used by Metropolia library to prevent and deter various theft actions to their valuable items and books. The scope of this thesis is limited to study different RFID based anti-theft technologies based on their power source, cost, reading range and deployment requirement. However, encryption and related security aspects are beyond the scope of this project. In addition, the project is only to study different cases of RFID based anti-theft implementation. Otherwise, there is no hardware or software design or related implementation including testing of the technology is conducted due to expensive cost constraint to buy the proposed RFID gate but propose measurement set-up that can be done in the future on entrance door of the fifth floor electronics laboratory corridor of Metropolia UAS campus. Thesis provides better understanding different types of RFID based anti-theft system suit-able for Electronic laboratory. As feature plan this thesis proposes the security gate to be interfaced using Lab view to Metropolia UAS Electronic Laboratory Database to store in-formation and monitor laboratory devices, components and too