Benchmarking Reidentification in Multi-Camera Tracking Systems with YOLOv8 and ResNet-50

The aim of this paper is to benchmark reidentification within a multi-camera tracking system. This benchmark has been carried out by leveraging transfer learning, utilizing YOLOv8 for real-time object detection and ResNet-50 for feature extraction. The objective is to evaluate the system's performance in accurately reidentifying vehicles across multiple cameras in real-world traffic surveillance scenarios. This benchmarking endeavor aims to provide an evaluation framework for assessing the capabilities and limitations of vehicle reidentification techniques, with a focus on their applicability in challenging conditions such as low- light environments, image compression, and object occlusions

A New Forgery Image Dataset and its Subjective Evaluation

The aim of this research paper is to present a new forgery image dataset with a thorough subjective evaluation in detecting manipulated images, considering various parameters. The original images were obtained from public sources, and meaningful forgeries were produced using an image editing plat- form with three techniques: cut-paste, copy-move, and erase-fill. Both pre-processing and post-processing methods were used to generate fake images. The subjective evaluation revealed that the accuracy of manipulated image detection was affected by various factors, such as user type, image quantity, tampering method, and image resolution, which were analyzed using quantitative data

Predicting Quality Of Experience For Online Video Systems Using Machine Learning

As the expansion of the online video broadcasting continues in every area of the modern connected world, the need for measuring and predicting the Quality of Experience for content delivery has never been this important. This demo paper has designed and developed a real-time and continuously trained machine learning model in order to predict QoE for online video systems. For this purpose, a platform has been developed where video content is unicasted to a cluster of users simultaneously while objective video metrics are collected into a database. At the end of each video, each user is queried with a subjective survey about their experience. Both quantitative statistics (video metrics) and qualitative information (user surveys) are used continuously as training data to machine learning model. The overall results show that proposed QoE estimation system provides an average Mean Opinion Score (MOS) precision with an error rate ranging from 12% to 15%. This methodology can efficiently answer the problem of predicting user experience for any online video delivery system, while overcoming the problematic interpretation of subjective consumer experience in terms of quantitative metrics

V2I Applications in Highways: How RSU Dimensioning Can Improve Service Delivery

This paper investigates the performance of Vehicle-to-Infrastructure (V2I) services over Vehicular Networks (VANETs) that are assisted by Road Side Units (RSU). More specifically, an analytical study of RSU dimensioning and a respective module is designed and developed in a simulated VANET environment. Two V2I application scenarios (e.g. car crash, spot weather) are considered in order to evaluate the impact of RSUs, vehicles’ size and speed and car crash start time and duration on applications’ performance. It is shown that the VANET network metrics (Packet Loss and Packet Delivery Ratio) are affected by the available MAC Bit rates and application scenarios. Mobility model metrics (Total Busy Time and Total CO2 Emissions) are also affected by the different application scenarios, number and type of vehicles

Energy-Aware IP Routing over SDN

The routing protocols play a vital role in saving energy, especially by minimizing the time a packet takes to travel from source to destination. The aim of energy-aware routing protocols is to select a route that engages routers in such a way that the overall energy consumption is minimized. In this paper, a relationship between resource utilization and energy consumption is stated, further, a resource-aware dynamic routing algorithm for SDN is proposed. The contribution of this paper is a queuing theory-based approach that measures the average waiting time of nodes and links based on their utilization and finds a path that costs the least time. The paper also proposes a framework for implementing routing algorithm over an SDN. Performance of the algorithm is verified using a GNS3 based implementation with an Opendaylight controller. © 2019 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

On the Modelling of CDNaaS Deployment

With the increasing demand for over the top media content, understanding user perception and Quality of Experience (QoE) estimation have become a major business necessity for service providers. Online video broadcasting is a multifaceted procedure and calculation of performance for the components that build up a streaming platform requires an overall understanding of the Content Delivery Network as a service (CDNaaS) concept. Therefore, to evaluate delivery quality and predicting user perception while considering NFV (Network Function Virtualization) and limited cloud resources, a relationship between these concepts is required. In this paper, a generalized mathematical model to calculate the success rate of different tiers of online video delivery system is presented. Furthermore, an algorithm that indicates the correct moment to switch between CDNs is provided to improve throughput efficiency while maintaining QoE and keeping the cloud hosting costs as lowest possible

Load-Balancing for Edge QoE-Based VNF Placement for OTT Video Streaming

© 2018 IEEE. Over The Top (OTT) service providers require platforms to support distributed, complex, cloud-oriented, scalable, micro-service based systems. Such systems require on-the-fly placement of Virtual Network Functions (VNF) to support streaming and transcoding of content based on QoE feedback provided by the end-user. This paper proposes a QoE Scheme to support on-the-fly virtual network functions deployment for OTT video streaming and transcoding. The QoE feedback considers limited cloud resources, transcoding requirements, throughput and latency. Both horizontal and vertical scaling strategies (including VM migration) are discussed to cover up availability and reliability of intermediate and edge Content Delivery Network (CDN) cache nodes

Edu-Cloud: On-the-fly Employability Skills as a Service

21st Century global job market competition requires Science, Technology, Engineering and Mathematics (STEM) university curricula to support both state-of-the-art technical and soft skills learning to improve graduate employment. This necessitates the transformation of the current teaching and learning methodology powered by a social and col- laborative platform to provide a social co-learning environment. This social co-learning will provide students with opportunities for self-enrichment while supporting their technical skills and hands-on needs. The platform must also provide the required lab infrastructure for hands-on experimentation. This paper proposes the design and implementation of a cloud based platform called Edu-Cloud. The Edu-Cloud has been designed to provide automated resource provisioning and perform on-the-fly deployment of scalable virtual network functions to stream multimedia content closer to the global learners. This would help to meet the specific learning needs of a group of global interconnected students with similar learning skills and abilities. The benchmarking performance results show that the proposed framework works efficiently while reducing primary network traffic by deploying resources closer to the users and support scalability for a global deployment scenario