Dynamische Sensorselektion zur auftragsorientierten Objektverfolgung in Kameranetzwerken

Im Rahmen dieser Arbeit wurden Methoden untersucht und entwickelt, die es ermöglichen sollen, Netzwerke intelligenter Kameras aufgabenorientiert zu organisieren und dynamisch anzupassen. Insbesondere wurden Techniken erarbeitet, welche ein System in die Lage versetzen sollen Personen in einem definierten Videoüberwachungsbereich anhand dynamischer Gruppierungen von mehreren Kameras multisensoriell zu erfassen, zu lokalisieren und sensorübergreifend zu verfolgen

Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation

Data-hunger and data-imbalance are two major pitfalls in many deep learning approaches. For example, on highly optimized production lines, defective samples are hardly acquired while non-defective samples come almost for free. The defects however often seem to resemble each other, e.g., scratches on different products may only differ in a few characteristics. In this work, we introduce a framework, Defect Transfer GAN (DT-GAN), which learns to represent defect types independent of and across various background products and yet can apply defect-specific styles to generate realistic defective images. An empirical study on the MVTec AD and two additional datasets showcase DT-GAN outperforms state-of-the-art image synthesis methods w.r.t. sample fidelity and diversity in defect generation. We further demonstrate benefits for a critical downstream task in manufacturing -- defect classification. Results show that the augmented data from DT-GAN provides consistent gains even in the few samples regime and reduces the error rate up to 51% compared to both traditional and advanced data augmentation methods.Comment: Accepted by BMVC 202

The wide-field, multiplexed, spectroscopic facility WEAVE : survey design, overview, and simulated implementation

Funding for the WEAVE facility has been provided by UKRI STFC, the University of Oxford, NOVA, NWO, Instituto de Astrofísica de Canarias (IAC), the Isaac Newton Group partners (STFC, NWO, and Spain, led by the IAC), INAF, CNRS-INSU, the Observatoire de Paris, Région Île-de-France, CONCYT through INAOE, Konkoly Observatory (CSFK), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Lund University, the Leibniz Institute for Astrophysics Potsdam (AIP), the Swedish Research Council, the European Commission, and the University of Pennsylvania.WEAVE, the new wide-field, massively multiplexed spectroscopic survey facility for the William Herschel Telescope, will see first light in late 2022. WEAVE comprises a new 2-degree field-of-view prime-focus corrector system, a nearly 1000-multiplex fibre positioner, 20 individually deployable 'mini' integral field units (IFUs), and a single large IFU. These fibre systems feed a dual-beam spectrograph covering the wavelength range 366-959 nm at R ∼ 5000, or two shorter ranges at R ∼ 20,000. After summarising the design and implementation of WEAVE and its data systems, we present the organisation, science drivers and design of a five- to seven-year programme of eight individual surveys to: (i) study our Galaxy's origins by completing Gaia's phase-space information, providing metallicities to its limiting magnitude for ∼ 3 million stars and detailed abundances for ∼ 1.5 million brighter field and open-cluster stars; (ii) survey ∼ 0.4 million Galactic-plane OBA stars, young stellar objects and nearby gas to understand the evolution of young stars and their environments; (iii) perform an extensive spectral survey of white dwarfs; (iv) survey  ∼ 400 neutral-hydrogen-selected galaxies with the IFUs; (v) study properties and kinematics of stellar populations and ionised gas in z 1 million spectra of LOFAR-selected radio sources; (viii) trace structures using intergalactic/circumgalactic gas at z > 2. Finally, we describe the WEAVE Operational Rehearsals using the WEAVE Simulator.PostprintPeer reviewe

The wide-field, multiplexed, spectroscopic facility WEAVE: Survey design, overview, and simulated implementation

WEAVE, the new wide-field, massively multiplexed spectroscopic survey facility for the William Herschel Telescope, will see first light in late 2022. WEAVE comprises a new 2-degree field-of-view prime-focus corrector system, a nearly 1000-multiplex fibre positioner, 20 individually deployable 'mini' integral field units (IFUs), and a single large IFU. These fibre systems feed a dual-beam spectrograph covering the wavelength range 366$-$959\,nm at $R\sim5000$, or two shorter ranges at $R\sim20\,000$. After summarising the design and implementation of WEAVE and its data systems, we present the organisation, science drivers and design of a five- to seven-year programme of eight individual surveys to: (i) study our Galaxy's origins by completing Gaia's phase-space information, providing metallicities to its limiting magnitude for $\sim$3 million stars and detailed abundances for $\sim1.5$ million brighter field and open-cluster stars; (ii) survey $\sim0.4$ million Galactic-plane OBA stars, young stellar objects and nearby gas to understand the evolution of young stars and their environments; (iii) perform an extensive spectral survey of white dwarfs; (iv) survey $\sim400$ neutral-hydrogen-selected galaxies with the IFUs; (v) study properties and kinematics of stellar populations and ionised gas in $z<0.5$ cluster galaxies; (vi) survey stellar populations and kinematics in $\sim25\,000$ field galaxies at $0.3\lesssim z \lesssim 0.7$; (vii) study the cosmic evolution of accretion and star formation using $>1$ million spectra of LOFAR-selected radio sources; (viii) trace structures using intergalactic/circumgalactic gas at $z>2$. Finally, we describe the WEAVE Operational Rehearsals using the WEAVE Simulator.Comment: 41 pages, 27 figures, accepted for publication by MNRA

The wide-field, multiplexed, spectroscopic facility WEAVE: Survey design, overview, and simulated implementation

WEAVE, the new wide-field, massively multiplexed spectroscopic survey facility for the William Herschel Telescope, will see first light in late 2022. WEAVE comprises a new 2-degree field-of-view prime-focus corrector system, a nearly 1000-multiplex fibre positioner, 20 individually deployable 'mini' integral field units (IFUs), and a single large IFU. These fibre systems feed a dual-beam spectrograph covering the wavelength range 366−959\,nm at R∼5000, or two shorter ranges at R∼20000. After summarising the design and implementation of WEAVE and its data systems, we present the organisation, science drivers and design of a five- to seven-year programme of eight individual surveys to: (i) study our Galaxy's origins by completing Gaia's phase-space information, providing metallicities to its limiting magnitude for ∼3 million stars and detailed abundances for ∼1.5 million brighter field and open-cluster stars; (ii) survey ∼0.4 million Galactic-plane OBA stars, young stellar objects and nearby gas to understand the evolution of young stars and their environments; (iii) perform an extensive spectral survey of white dwarfs; (iv) survey ∼400 neutral-hydrogen-selected galaxies with the IFUs; (v) study properties and kinematics of stellar populations and ionised gas in z1 million spectra of LOFAR-selected radio sources; (viii) trace structures using intergalactic/circumgalactic gas at z>2. Finally, we describe the WEAVE Operational Rehearsals using the WEAVE Simulator

Illumination invariant background subtraction for Pan/Tilt cameras using DoG responses

In this paper an efficient and robust illumination invariant background subtraction approach for pan/tilt cameras is introduced. In a preprocessing step a panorama-based approach for temporal pixel registration is used to obtain a joint motion independent background model. Hereby, inconsistencies in the background model occur due to alternating illumination, automatic white balancing, AGC, as well as lens vignetting artefacts. During background subtraction such inconsistencies and artefacts lead to segmentation clutter and as a consequence increase false detection rates. To overcome these problems, in this paper an illumination normalization method based on Difference-Of-Gaussian (DoG) band-pass filters is proposed. By preprocessing camera images by the proposed filters lens vignetting artefacts, as well as AGC and global illumination changes are eliminated robustly, and simultaneously local color and intensity information are preserved for accurate motion detection

Dynamische Sensorselektion zur auftragsorientierten Objektverfolgung in Kameranetzwerken

Im Rahmen dieser Arbeit wurden Methoden untersucht und entwickelt, die es ermöglichen sollen, Netzwerke intelligenter Kameras aufgabenorientiert zu organisieren und dynamisch anzupassen. Insbesondere wurden Techniken erarbeitet, welche ein System in die Lage versetzen sollen Personen in einem definierten Videoüberwachungsbereich anhand dynamischer Gruppierungen von mehreren Kameras multisensoriell zu erfassen, zu lokalisieren und sensorübergreifend zu verfolgen

The crowd congestion level - a new measure for risk assessment in video-based crowd monitoring

In this paper, we propose a new characteristic measure relative people density and motion dynamics for the purpose of long-term crowd monitoring. While many related works focus on direct people counting and absolute density estimation, we will show that relative densities provide reliable information on crowd behaviour. Furthermore, we will discuss the derivation of a so-called Congestion Level of local areas in the crowd, which takes the current dynamics and density within a certain image region into account. Our density estimation approach is based on a well-known KLT feature tracking algorithm, combined with a post-processing for motion vector association. The resulting feature tracks (tracklets) represent movements of detected objects in the scene. These trajectories are used as basic features for later estimation of track density and relative inertia (changes in motion dynamics), which together are combined to a joint Congestion Level. We show the results of our approach by comparing the characteristic measures of track density and Congestion Level with manually annotated Ground truth data of both artificial and real scenes

A soft-biometrics dataset for person tracking and re-identification

In this work we present a new dataset for the tasks person detection, tracking, re-identification, and soft-biometric attribute detection in surveillance data. The dataset was recorded over three days and consists of more than 30 individuals moving through a network of seven cameras. Person tracks are labeled with consistent IDs as well as softbiometric attributes, such as a description of the clothing, gender, or height. Persons in the video data alter their appearance by changing clothes or wearing accessories. A second, clothing specific ID of each track allows for the evaluation of re-identification with or without the presence of clothing changes. In addition to video and camera calibration data, we provide evaluation protocols, tools and baseline results for each of the four tasks

Ein auftragsorientierter Ansatz zur kameraübergreifenden Personenverfolgung in verteilten Kameranetzwerken

A generic approach and process architecture for task oriented sensor data processing is presented Hereby, we focus on the application of video surveillance in large camera networks Firstly, advantages of the proposed task oriented video processing, compared to today s (sensor oriented) video surveillance systems, are described Secondly, a generic process framework is presented, which has been designed for task oriented video processing Finally, system components of the task oriented frame work needed for the task of multi camera person tracking are introduced