1,394 research outputs found
Long Range Automated Persistent Surveillance
This dissertation addresses long range automated persistent surveillance with focus on three topics: sensor planning, size preserving tracking, and high magnification imaging.
field of view should be reserved so that camera handoff can be executed successfully before the object of interest becomes unidentifiable or untraceable. We design a sensor planning algorithm that not only maximizes coverage but also ensures uniform and sufficient overlapped cameraās field of view for an optimal handoff success rate. This algorithm works for environments with multiple dynamic targets using different types of cameras. Significantly improved handoff success rates are illustrated via experiments using floor plans of various scales.
Size preserving tracking automatically adjusts the cameraās zoom for a consistent view of the object of interest. Target scale estimation is carried out based on the paraperspective projection model which compensates for the center offset and considers system latency and tracking errors. A computationally efficient foreground segmentation strategy, 3D affine shapes, is proposed. The 3D affine shapes feature direct and real-time implementation and improved flexibility in accommodating the targetās 3D motion, including off-plane rotations. The effectiveness of the scale estimation and foreground segmentation algorithms is validated via both offline and real-time tracking of pedestrians at various resolution levels.
Face image quality assessment and enhancement compensate for the performance degradations in face recognition rates caused by high system magnifications and long observation distances. A class of adaptive sharpness measures is proposed to evaluate and predict this degradation. A wavelet based enhancement algorithm with automated frame selection is developed and proves efficient by a considerably elevated face recognition rate for severely blurred long range face images
Enabling Runtime Self-Coordination of Reconfigurable Embedded Smart Cameras in Distributed Networks
Smart camera networks are real-time distributed embedded systems able to perform computer vision using multiple cameras. This new approach is a confluence of four major disciplines (computer vision, image sensors, embedded computing and sensor networks) and has been subject of intensive work in the past decades. The recent advances in computer vision and network communication, and the rapid growing in the field of high-performance computing, especially using reconfigurable devices, have enabled the design of more robust smart camera systems. Despite these advancements, the effectiveness of current networked vision systems (compared to their operating costs) is still disappointing; the main reason being the poor coordination among cameras entities at runtime and the lack of a clear formalism to dynamically capture and address the self-organization problem without relying on human intervention. In this dissertation, we investigate the use of a declarative-based modeling approach for capturing runtime self-coordination. We combine modeling approaches borrowed from logic programming, computer vision techniques, and high-performance computing for the design of an autonomous and cooperative smart camera. We propose a compact modeling approach based on Answer Set Programming for architecture synthesis of a system-on-reconfigurable-chip camera that is able to support the runtime cooperative work and collaboration with other camera nodes in a distributed network setup. Additionally, we propose a declarative approach for modeling runtime camera self-coordination for distributed object tracking in which moving targets are handed over in a distributed manner and recovered in case of node failure
Non-linear echo cancellation - a Bayesian approach
Echo cancellation literature is reviewed, then a Bayesian model is introduced and it is shown how how it can be used to model and fit nonlinear channels. An algorithm for cancellation of echo over a nonlinear channel is developed and tested. It is shown that this nonlinear algorithm converges for both linear and nonlinear channels and is superior to linear echo cancellation for canceling an echo through a nonlinear echo-path channel
Edge Computing for Extreme Reliability and Scalability
The massive number of Internet of Things (IoT) devices and their continuous data collection will lead to a rapid increase in the scale of collected data. Processing all these collected data at the central cloud server is inefficient, and even is unfeasible or unnecessary. Hence, the task of processing the data is pushed to the network edges introducing the concept of Edge Computing. Processing the information closer to the source of data (e.g., on gateways and on edge micro-servers) not only reduces the huge workload of central cloud, also decreases the latency for real-time applications by avoiding the unreliable and unpredictable network latency to communicate with the central cloud
A negotiation protocol with conditional offers for camera handoffs.
This thesis explores the idea of conditional offers during camera handoff negotiations. In a
departure from contract-net inspired negotiation models that have been proposed for camera
handoffs, the current scheme assumes that each camera maintains the state of its neighbouring
cameras. To this end, we develop a new short-term memory model for maintaining a camera???s
own state and the state of its neighbouring cameras. The fact that each camera is aware of its
surrounding cameras is exploited to generate conditional offers during handoff negotiations.
This can result in multiple rounds of negotiations during a single handoff, leading to successful
handoffs in situations where one of the cameras that is being asked to take on one more task
is unable to take on a new task without relinquishing an existing task. The results demonstrate
the advantages of the proposed negotiation model over existing models for camera handoffs
Veliki nadzorni sustav: detekcija i praÄenje sumnjivih obrazaca pokreta u prometnim gužvama
The worldwide increasing sentiment of insecurity gave birth to a new era, shaking thereby the intelligent video-surveillance systems design and deployment. The large-scale use of these means has prompted the creation of new needs in terms of analysis and interpretation. For this purpose, behavior recognition and scene understanding related applications have become more captivating to a significant number of computer vision researchers, particularly when crowded scenes are concerned. So far, motion analysis and tracking remain challenging due to significant visual ambiguities, which encourage looking into further keys. By this work, we present a new framework to recognize various motion patterns, extract abnormal behaviors and track them over a multi-camera traffic surveillance system. We apply a density-based technique to cluster motion vectors produced by optical flow, and compare them with motion pattern models defined earlier. Non-identified clusters are treated as suspicious and simultaneously tracked over an overlapping camera network for as long as possible. To aiming the network configuration, we designed an active camera scheduling strategy where camera assignment was realized via an improved Weighted Round-Robin algorithm. To validate our approach, experiment results are presented and discussed.Å irom svijeta rasprostranjeni osjeÄaj nesigurnosti postavio je temelje za dizajniranje i implementaciju inteligentnih sustava nadzora. Velika upotreba ovih sredstava potaknula je stvaranje novih potreba analize i interpretacije. U ovu svrhu, prepoznavanje ponaÅ”anja i razumijevanje prizora postaju sve privlaÄnije povezane primjene znaÄajnom broju istraživaÄa raÄunalne vizije, posebno kada se radi o vrlo prometnim prizorima. Analiza pokreta i slijeÄenja ostalo je izazovno podruÄje zbog znaÄajnih vizualnih nejasnoÄa koje zahtijevaju daljnja istraživanja. U radu je prikazan novi okvir za prepoznavanje razliÄitih uzoraka pokreta, izoliranje neprirodnih ponaÅ”anja i njihovo praÄenje pomoÄu nadzornog sustava prometa s viÅ”e kamera. Primjenjuje se na gustoÄi zasnovana tehnika skupa vektora pokreta sastavljenih iz optiÄkog toka te usporeÄenih s ranije definiranim modelima uzoraka. Neidentificirani skupovi tretiraju se kao sumnjivi i istovremeno su praÄeni mrežom s viÅ”e preklapajuÄih kamera Å”to je duže moguÄe. S ciljem konfiguriranja mreže, dizajnirana je strategija rasporeÄivanja aktivnih kamera gdje je dodjela kamere ostvarena pomoÄu unaprijeÄenog "Weighted Round-Robin" algoritma
Multimedia
The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications
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