Gait analysis from encrypted video surveillance traffic

This thesis proposes an original video-based gait analysis technique, different from others existing in the literature. We leverage deep learning techniques to analyze video sequence packet size both in a virtual and real environment. Moreover, we address the case in which encryption mechanisms are adopted and we conclude the study proposing an incremental learning framework to render the system suitable to real life applications where training data becomes progressively available over time.ope

A reduced reference video quality assessment method for provision as a service over SDN/NFV-enabled networks

139 p.The proliferation of multimedia applications and services has generarted a noteworthy upsurge in network traffic regarding video content and has created the need for trustworthy service quality assessment methods. Currently, predominent position among the technological trends in telecommunication networkds are Network Function Virtualization (NFV), Software Defined Networking (SDN) and 5G mobile networks equipped with small cells. Additionally Video Quality Assessment (VQA) methods are a very useful tool for both content providers and network operators, to understand of how users perceive quality and this study the feasibility of potential services and adapt the network available resources to satisfy the user requirements

A reduced reference video quality assessment method for provision as a service over SDN/NFV-enabled networks

139 p.The proliferation of multimedia applications and services has generarted a noteworthy upsurge in network traffic regarding video content and has created the need for trustworthy service quality assessment methods. Currently, predominent position among the technological trends in telecommunication networkds are Network Function Virtualization (NFV), Software Defined Networking (SDN) and 5G mobile networks equipped with small cells. Additionally Video Quality Assessment (VQA) methods are a very useful tool for both content providers and network operators, to understand of how users perceive quality and this study the feasibility of potential services and adapt the network available resources to satisfy the user requirements

Foveated Video Streaming for Cloud Gaming

Video gaming is generally a computationally intensive application and to provide a pleasant user experience specialized hardware like Graphic Processing Units may be required. Computational resources and power consumption are constraints which limit visually complex gaming on, for example, laptops, tablets and smart phones. Cloud gaming may be a possible approach towards providing a pleasant gaming experience on thin clients which have limited computational and energy resources. In a cloud gaming architecture, the game-play video is rendered and encoded in the cloud and streamed to a client where it is displayed. User inputs are captured at the client and streamed back to the server, where they are relayed to the game. High quality of experience requires the streamed video to be of high visual quality which translates to substantial downstream bandwidth requirements. The visual perception of the human eye is non-uniform, being maximum along the optical axis of the eye and dropping off rapidly away from it. This phenomenon, called foveation, makes the practice of encoding all areas of a video frame with the same resolution wasteful. In this thesis, foveated video streaming from a cloud gaming server to a cloud gaming client is investigated. A prototype cloud gaming system with foveated video streaming is implemented. The cloud gaming server of the prototype is configured to encode gameplay video in a foveated fashion based on gaze location data provided by the cloud gaming client. The effect of foveated encoding on the output bitrate of the streamed video is investigated. Measurements are performed using games from various genres and with different player points of view to explore changes in video bitrate with different parameters of foveation. Latencies involved in foveated video streaming for cloud gaming, including latency of the eye tracker used in the thesis, are also briefly discussed

Optimisation énergétique de processus de traitement du signal et ses applications au décodage vidéo

Consumer electronics offer today more and more features (video, audio, GPS, Internet) and connectivity means (multi-radio systems with WiFi, Bluetooth, UMTS, HSPA, LTE-advanced ... ). The power demand of these devices is growing for the digital part especially for the processing chip. To support this ever increasing computing demand, processor architectures have evolved with multicore processors, graphics processors (GPU) and ether dedicated hardware accelerators. However, the evolution of battery technology is itself slower. Therefore, the autonomy of embedded systems is now under a great pressure. Among the new functionalities supported by mobile devices, video services take a prominent place. lndeed, recent analyzes show that they will represent 70% of mobile Internet traffic by 2016. Accompanying this growth, new technologies are emerging for new services and applications. Among them HEVC (High Efficiency Video Coding) can double the data compression while maintaining a subjective quality equivalent to its predecessor, the H.264 standard. ln a digital circuit, the total power consumption is made of static power and dynamic power. Most of modern hardware architectures implement means to control the power consumption of the system. Dynamic Voltage and Frequency Scaling (DVFS) mainly reduces the dynamic power of the circuit. This technique aims to adapt the power of the processor (and therefore its consumption) to the actual load needed by the application. To control the static power, Dynamic Power Management (DPM or sleep modes) aims to stop the voltage supplies associated with specific areas of the chip. ln this thesis, we first present a model of the energy consumed by the circuit integrating DPM and DVFS modes. This model is generalized to multi-core integrated circuits and to a rapid prototyping tool. Thus, the optimal operating point of a circuit, i.e. the operating frequency and the number of active cores, is identified. Secondly, the HEVC application is integrated to a multicore architecture coupled with a sophisticated DVFS mechanism. We show that this application can be implemented efficiently on general purpose processors (GPP) while minimizing the power consumption. Finally, and to get further energy gain, we propose a modified HEVC decoder that is capable to tune its energy gains together with a decoding quality trade-off.Aujourd'hui, les appareils électroniques offrent de plus en plus de fonctionnalités (vidéo, audio, GPS, internet) et des connectivités variées (multi-systèmes de radio avec WiFi, Bluetooth, UMTS, HSPA, LTE-advanced ... ). La demande en puissance de ces appareils est donc grandissante pour la partie numérique et notamment le processeur de calcul. Pour répondre à ce besoin sans cesse croissant de nouvelles fonctionnalités et donc de puissance de calcul, les architectures des processeurs ont beaucoup évolué : processeurs multi-coeurs, processeurs graphiques (GPU) et autres accélérateurs matériels dédiés. Cependant, alors que de nouvelles architectures matérielles peinent à répondre aux exigences de performance, l'évolution de la technologie des batteries est quant à elle encore plus lente. En conséquence, l'autonomie des systèmes embarqués est aujourd'hui sous pression. Parmi les nouveaux services supportés par les terminaux mobiles, la vidéo prend une place prépondérante. En effet, des analyses récentes de tendance montrent qu'elle représentera 70 % du trafic internet mobile dès 2016. Accompagnant cette croissance, de nouvelles technologies émergent permettant de nouveaux services et applications. Parmi elles, HEVC (High Efficiency Video Coding) permet de doubler la compression de données tout en garantissant une qualité subjective équivalente à son prédécesseur, la norme H.264. Dans un circuit numérique, la consommation provient de deux éléments: la puissance statique et la puissance dynamique. La plupart des architectures matérielles récentes mettent en oeuvre des procédés permettant de contrôler la puissance du système. Le changement dynamique du couple tension/fréquence appelé Dynamic Voltage and Frequency Scaling (DVFS) agit principalement sur la puissance dynamique du circuit. Cette technique permet d'adapter la puissance du processeur (et donc sa consommation) à la charge réelle nécessaire pour une application. Pour contrôler la puissance statique, le Dynamic Power Management (DPM, ou modes de veille) consistant à arrêter les alimentations associées à des zones spécifiques de la puce. Dans cette thèse, nous présentons d'abord une modélisation de l'énergie consommée par le circuit intégrant les modes DVFS et DPM. Cette modélisation est généralisée au circuit multi-coeurs et intégrée à un outil de prototypage rapide. Ainsi le point de fonctionnement optimal d'un circuit, la fréquence de fonctionnement et le nombre de coeurs actifs, est identifié. Dans un second temps, l'application HEVC est intégrée à une architecture multi-coeurs avec une adaptation dynamique de la fréquence de développement. Nous montrons que cette application peut être implémentée efficacement sur des processeurs généralistes (GPP) tout en minimisant la puissance consommée. Enfin, et pour aller plus loin dans les gains en énergie, nous proposons une modification du décodeur HEVC qui permet à un décodeur de baisser encore plus sa consommation en fonction du budget énergétique disponible localement

A selective approach for energy-aware video content adaptation decision-taking engine in android based smartphone

Rapid advancement of technology and their increasing affordability have transformed mobile devices from a means of communication to tools for socialization, entertainment, work and learning. However, advancement of battery technology and capacity is slow compared to energy need. Viewing content with high quality of experience will consume high power. In limited available energy, normal content adaptation system will decrease the content quality, hence reducing quality of experience. However, there is a need for optimizing content quality of experience (QoE) in a limited available energy. With modification and improvement, content adaptation may solve this issue. The key objective of this research is to propose a framework for energy-aware video content adaptation system to enable video delivery over the Internet. To optimise the QoE while viewing streaming video on a limited available smartphone energy, an algorithm for energy-aware video content adaptation decision-taking engine named EnVADE is proposed. The EnVADE algorithm uses selective mechanism. Selective mechanism means the video segmented into scenes and adaptation process is done based on the selected scenes. Thus, QoE can be improved. To evaluate EnVADE algorithm in term of energy efficiency, an experimental evaluation has been done. Subjective evaluation by selected respondents are also has been made using Absolute Category Rating method as recommended by ITU to evaluate EnVADE algorithm in term of QoE. In both evaluation, comparison with other methods has been made. The results show that the proposed solution is able to increase the viewing time of about 14% compared to MPEG-DASH which is an official international standard and widely used streaming method. In term of QoE subjective test, EnVADE algorithm score surpasses the score of other video streaming method. Therefore, EnVADE framework and algorithm has proven its capability as an alternative technique to stream video content with higher QoE and lower energy consumption

Smart Sensor Technologies for IoT

The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT