Autonomous monitoring framework for resource-constrained environments

Acknowledgments The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub, reference: EP/G066051/1. URL: http://www.dotrural.ac.uk/RemoteStream/Peer reviewedPublisher PD

Dynamic Reconfiguration in Camera Networks: A Short Survey

There is a clear trend in camera networks towards enhanced functionality and flexibility, and a fixed static deployment is typically not sufficient to fulfill these increased requirements. Dynamic network reconfiguration helps to optimize the network performance to the currently required specific tasks while considering the available resources. Although several reconfiguration methods have been recently proposed, e.g., for maximizing the global scene coverage or maximizing the image quality of specific targets, there is a lack of a general framework highlighting the key components shared by all these systems. In this paper we propose a reference framework for network reconfiguration and present a short survey of some of the most relevant state-of-the-art works in this field, showing how they can be reformulated in our framework. Finally we discuss the main open research challenges in camera network reconfiguration

Video Surveillance Analysis as a Context for Embedded Systems and Artificial Intelligence Education

Video surveillance analysis is an exciting, active research area and an important industry application. It is a multidisciplinary field that draws on signal processing, embedded systems, and artificial intelligence topics, and is well suited to motivate student engagement in all of these areas. This paper describes the benefits of the convergence of these topics, presents a versatile video surveillance analysis process that can be used as the basis for many investigations, and presents two template exercises in tracking detected targets and in evaluating runtime efficiency. The processing chain consists of detecting changes in a scene and locating and characterizing the resulting targets. The analysis is illustrated for targets in outdoor scenes using a variety of classification features. Also, sample code for processing is included

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Quality management of surveillance multimedia streams via federated SDN controllers in Fiwi-iot integrated deployment environments

Traditionally, hybrid optical-wireless networks (Fiber-Wireless - FiWi domain) and last-mile Internet of Things edge networks (Edge IoT domain) have been considered independently, with no synergic management solutions. On the one hand, FiWi has primarily focused on high-bandwidth and low-latency access to cellular-equipped nodes. On the other hand, Edge IoT has mainly aimed at effective dispatching of sensor/actuator data among (possibly opportunistic) nodes, by using direct peer-to-peer and base station (BS)-assisted Internet communications. The paper originally proposes a model and an architecture that loosely federate FiWi and Edge IoT domains based on the interaction of FiWi and Edge IoT software defined networking controllers: The primary idea is that our federated controllers can seldom exchange monitoring data and control hints the one with the other, thus mutually enhancing their capability of end-to-end quality-aware packet management. To show the applicability and the effectiveness of the approach, our original proposal is applied to the notable example of multimedia stream provisioning from surveillance cameras deployed in the Edge IoT domain to both an infrastructure-side server and spontaneously interconnected mobile smartphones; our solution is able to tune the BS behavior of the FiWi domain and to reroute/prioritize traffic in the Edge IoT domain, with the final goal to reduce latency. In addition, the reported application case shows the capability of our solution of joint and coordinated exploitation of resources in FiWi and Edge IoT domains, with performance results that highlight its benefits in terms of efficiency and responsiveness

A Survey on Multimedia-Based Cross-Layer Optimization in Visual Sensor Networks

Visual sensor networks (VSNs) comprised of battery-operated electronic devices endowed with low-resolution cameras have expanded the applicability of a series of monitoring applications. Those types of sensors are interconnected by ad hoc error-prone wireless links, imposing stringent restrictions on available bandwidth, end-to-end delay and packet error rates. In such context, multimedia coding is required for data compression and error-resilience, also ensuring energy preservation over the path(s) toward the sink and improving the end-to-end perceptual quality of the received media. Cross-layer optimization may enhance the expected efficiency of VSNs applications, disrupting the conventional information flow of the protocol layers. When the inner characteristics of the multimedia coding techniques are exploited by cross-layer protocols and architectures, higher efficiency may be obtained in visual sensor networks. This paper surveys recent research on multimedia-based cross-layer optimization, presenting the proposed strategies and mechanisms for transmission rate adjustment, congestion control, multipath selection, energy preservation and error recovery. We note that many multimedia-based cross-layer optimization solutions have been proposed in recent years, each one bringing a wealth of contributions to visual sensor networks

A Cyber-Physical System for integrated remote control andprotection of smart grid critical infrastructures

This work proposes a Cyber-Physical System (CPS) for protecting Smart Electric Grid Critical Infrastructures (CI) using video surveillance while remotely monitoring them. Due to the critical nature of Smart Grid, it is necessary to guarantee an adequate level of safety, security and reliability. Thus, this CPS is back-boned by a Time-Sensitive Network solution (TSN) providing concurrent support for smart-video surveillance and Smart Grid control over a single communication infrastructure. To this end, TSN delivers high-bandwidth communication for video surveil-lance and deterministic Quality of Service (QoS), latency and bandwidth guarantees, required by the time-critical Smart Grid control. On the one hand, the CPS utilizes High-availability Seamless Redundancy (HSR) in the control subsystem via Remote Terminal Units (RTU) guaranteeing seamless failover against failures in Smart Grid. On the other hand, the smart video surveillance subsystem applies machine learning to monitor secured perimeters and detect people around the Smart Grid CI. Moreover, it is also able to directly interoperate with RTUs via MODBUS protocol to send alarms in case of e.g. intrusion. The work evaluates the accuracy and performance of the detection using common metrics in surveillance field. An integrated monitoring dashboard has also been developed in which all CPS information is available in real timeThis work was partially supported by the EU Project FitOptiVis [3] through the ECSEL Joint Undertaking under GA n. 783162, a Spanish National grant funded by MINECO through APCIN PCI2018-093184, and partially by the Research Network RED2018-102511-

Towards Developing Computer Vision Algorithms and Architectures for Real-world Applications

abstract: Computer vision technology automatically extracts high level, meaningful information from visual data such as images or videos, and the object recognition and detection algorithms are essential in most computer vision applications. In this dissertation, we focus on developing algorithms used for real life computer vision applications, presenting innovative algorithms for object segmentation and feature extraction for objects and actions recognition in video data, and sparse feature selection algorithms for medical image analysis, as well as automated feature extraction using convolutional neural network for blood cancer grading. To detect and classify objects in video, the objects have to be separated from the background, and then the discriminant features are extracted from the region of interest before feeding to a classifier. Effective object segmentation and feature extraction are often application specific, and posing major challenges for object detection and classification tasks. In this dissertation, we address effective object flow based ROI generation algorithm for segmenting moving objects in video data, which can be applied in surveillance and self driving vehicle areas. Optical flow can also be used as features in human action recognition algorithm, and we present using optical flow feature in pre-trained convolutional neural network to improve performance of human action recognition algorithms. Both algorithms outperform the state-of-the-arts at their time. Medical images and videos pose unique challenges for image understanding mainly due to the fact that the tissues and cells are often irregularly shaped, colored, and textured, and hand selecting most discriminant features is often difficult, thus an automated feature selection method is desired. Sparse learning is a technique to extract the most discriminant and representative features from raw visual data. However, sparse learning with \textit{L1} regularization only takes the sparsity in feature dimension into consideration; we improve the algorithm so it selects the type of features as well; less important or noisy feature types are entirely removed from the feature set. We demonstrate this algorithm to analyze the endoscopy images to detect unhealthy abnormalities in esophagus and stomach, such as ulcer and cancer. Besides sparsity constraint, other application specific constraints and prior knowledge may also need to be incorporated in the loss function in sparse learning to obtain the desired results. We demonstrate how to incorporate similar-inhibition constraint, gaze and attention prior in sparse dictionary selection for gastroscopic video summarization that enable intelligent key frame extraction from gastroscopic video data. With recent advancement in multi-layer neural networks, the automatic end-to-end feature learning becomes feasible. Convolutional neural network mimics the mammal visual cortex and can extract most discriminant features automatically from training samples. We present using convolutinal neural network with hierarchical classifier to grade the severity of Follicular Lymphoma, a type of blood cancer, and it reaches 91\% accuracy, on par with analysis by expert pathologists. Developing real world computer vision applications is more than just developing core vision algorithms to extract and understand information from visual data; it is also subject to many practical requirements and constraints, such as hardware and computing infrastructure, cost, robustness to lighting changes and deformation, ease of use and deployment, etc.The general processing pipeline and system architecture for the computer vision based applications share many similar design principles and architecture. We developed common processing components and a generic framework for computer vision application, and a versatile scale adaptive template matching algorithm for object detection. We demonstrate the design principle and best practices by developing and deploying a complete computer vision application in real life, building a multi-channel water level monitoring system, where the techniques and design methodology can be generalized to other real life applications. The general software engineering principles, such as modularity, abstraction, robust to requirement change, generality, etc., are all demonstrated in this research.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Flexi-WVSNP-DASH: A Wireless Video Sensor Network Platform for the Internet of Things

abstract: Video capture, storage, and distribution in wireless video sensor networks (WVSNs) critically depends on the resources of the nodes forming the sensor networks. In the era of big data, Internet of Things (IoT), and distributed demand and solutions, there is a need for multi-dimensional data to be part of the Sensor Network data that is easily accessible and consumable by humanity as well as machinery. Images and video are expected to become as ubiquitous as is the scalar data in traditional sensor networks. The inception of video-streaming over the Internet, heralded a relentless research for effective ways of distributing video in a scalable and cost effective way. There has been novel implementation attempts across several network layers. Due to the inherent complications of backward compatibility and need for standardization across network layers, there has been a refocused attention to address most of the video distribution over the application layer. As a result, a few video streaming solutions over the Hypertext Transfer Protocol (HTTP) have been proposed. Most notable are Apple’s HTTP Live Streaming (HLS) and the Motion Picture Experts Groups Dynamic Adaptive Streaming over HTTP (MPEG-DASH). These frameworks, do not address the typical and future WVSN use cases. A highly flexible Wireless Video Sensor Network Platform and compatible DASH (WVSNP-DASH) are introduced. The platform's goal is to usher video as a data element that can be integrated into traditional and non-Internet networks. A low cost, scalable node is built from the ground up to be fully compatible with the Internet of Things Machine to Machine (M2M) concept, as well as the ability to be easily re-targeted to new applications in a short time. Flexi-WVSNP design includes a multi-radio node, a middle-ware for sensor operation and communication, a cross platform client facing data retriever/player framework, scalable security as well as a cohesive but decoupled hardware and software design.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Gabor-enhanced histogram of oriented gradients for human presence detection applied in aerial monitoring

In UAV-based human detection, the extraction and selection of the feature vector are one of the critical tasks to ensure the optimal performance of the detection system. Although UAV cameras capture high-resolution images, human figures' relative size renders persons at very low resolution and contrast. Feature descriptors that can adequately discriminate between local symmetrical patterns in a low-contrast image may improve a human figures' detection in vegetative environments. Such a descriptor is proposed and presented in this paper. Initially, the acquired images are fed to a digital processor in a ground station where the human detection algorithm is performed. Part of the human detection algorithm is the GeHOG feature extraction, where a bank of Gabor filters is used to generate textured images from the original. The local energy for each cell of the Gabor images is calculated to identify the dominant orientations. The bins of conventional HOG are enhanced based on the dominant orientation index and the accumulated local energy in Gabor images. To measure the performance of the proposed features, Gabor-enhanced HOG (GeHOG) and other two recent improvements to HOG, Histogram of Edge Oriented Gradients (HEOG) and Improved HOG (ImHOG), are used for human detection on INRIA dataset and a custom dataset of farmers working in fields captured via unmanned aerial vehicle. The proposed feature descriptor significantly improved human detection and performed better than recent improvements in conventional HOG. Using GeHOG improved the precision of human detection to 98.23% in the INRIA dataset. The proposed feature can significantly improve human detection applied in surveillance systems, especially in vegetative environments