438 research outputs found
Latent-Class Hough Forests for 3D object detection and pose estimation of rigid objects
In this thesis we propose a novel framework, Latent-Class Hough Forests, for the problem of 3D object detection and pose estimation in heavily cluttered and occluded scenes. Firstly, we adapt the state-of-the-art template-based representation, LINEMOD [34, 36], into a scale-invariant patch descriptor and integrate it into a regression forest using a novel template-based split function. In training, rather than explicitly collecting representative negative samples, our method is trained on positive samples only and we treat the class distributions at the leaf nodes as latent variables. During the inference process we iteratively update these distributions, providing accurate estimation of background clutter and foreground occlusions and thus a better detection rate. Furthermore, as a by-product, the latent class distributions can provide accurate occlusion aware segmentation masks, even in the multi-instance scenario. In addition to an existing public dataset, which contains only single-instance sequences with large amounts of clutter, we have collected a new, more challenging, dataset for multiple-instance detection containing heavy 2D and 3D clutter as well as foreground occlusions. We evaluate the Latent-Class Hough Forest on both of these datasets where we outperform state-of-the art methods.Open Acces
CAPTCHA Types and Breaking Techniques: Design Issues, Challenges, and Future Research Directions
The proliferation of the Internet and mobile devices has resulted in
malicious bots access to genuine resources and data. Bots may instigate
phishing, unauthorized access, denial-of-service, and spoofing attacks to
mention a few. Authentication and testing mechanisms to verify the end-users
and prohibit malicious programs from infiltrating the services and data are
strong defense systems against malicious bots. Completely Automated Public
Turing test to tell Computers and Humans Apart (CAPTCHA) is an authentication
process to confirm that the user is a human hence, access is granted. This
paper provides an in-depth survey on CAPTCHAs and focuses on two main things:
(1) a detailed discussion on various CAPTCHA types along with their advantages,
disadvantages, and design recommendations, and (2) an in-depth analysis of
different CAPTCHA breaking techniques. The survey is based on over two hundred
studies on the subject matter conducted since 2003 to date. The analysis
reinforces the need to design more attack-resistant CAPTCHAs while keeping
their usability intact. The paper also highlights the design challenges and
open issues related to CAPTCHAs. Furthermore, it also provides useful
recommendations for breaking CAPTCHAs
Real-time 3D hand reconstruction in challenging scenes from a single color or depth camera
Hands are one of the main enabling factors for performing complex tasks and humans naturally use them for interactions with their environment. Reconstruction and digitization of 3D hand motion opens up many possibilities for important applications. Hands gestures can be directly used for humanâcomputer interaction, which is especially relevant for controlling augmented or virtual reality (AR/VR) devices where immersion is of utmost importance. In addition, 3D hand motion capture is a precondition for automatic sign-language translation, activity recognition, or teaching robots. Different approaches for 3D hand motion capture have been actively researched in the past. While being accurate, gloves and markers are intrusive and uncomfortable to wear. Hence, markerless hand reconstruction based on cameras is desirable. Multi-camera setups provide rich input, however, they are hard to calibrate and lack the flexibility for mobile use cases. Thus, the majority of more recent methods uses a single color or depth camera which, however, makes the problem harder due to more ambiguities in the input. For interaction purposes, users need continuous control and immediate feedback. This means the algorithms have to run in real time and be robust in uncontrolled scenes. These requirements, achieving 3D hand reconstruction in real time from a single camera in general scenes, make the problem significantly more challenging. While recent research has shown promising results, current state-of-the-art methods still have strong limitations. Most approaches only track the motion of a single hand in isolation and do not take background-clutter or interactions with arbitrary objects or the other hand into account. The few methods that can handle more general and natural scenarios run far from real time or use complex multi-camera setups. Such requirements make existing methods unusable for many aforementioned applications. This thesis pushes the state of the art for real-time 3D hand tracking and reconstruction in general scenes from a single RGB or depth camera. The presented approaches explore novel combinations of generative hand models, which have been used successfully in the computer vision and graphics community for decades, and powerful cutting-edge machine learning techniques, which have recently emerged with the advent of deep learning. In particular, this thesis proposes a novel method for hand tracking in the presence of strong occlusions and clutter, the first method for full global 3D hand tracking from in-the-wild RGB video, and a method for simultaneous pose and dense shape reconstruction of two interacting hands that, for the first time, combines a set of desirable properties previously unseen in the literature.HĂ€nde sind einer der Hauptfaktoren fĂŒr die AusfĂŒhrung komplexer Aufgaben, und Menschen verwenden sie auf natĂŒrliche Weise fĂŒr Interaktionen mit ihrer Umgebung. Die Rekonstruktion und Digitalisierung der 3D-Handbewegung eröffnet viele Möglichkeiten fĂŒr wichtige Anwendungen. Handgesten können direkt als Eingabe fĂŒr die Mensch-Computer-Interaktion verwendet werden. Dies ist insbesondere fĂŒr GerĂ€te der erweiterten oder virtuellen RealitĂ€t (AR / VR) relevant, bei denen die Immersion von gröĂter Bedeutung ist. DarĂŒber hinaus ist die Rekonstruktion der 3D Handbewegung eine Voraussetzung zur automatischen Ăbersetzung von GebĂ€rdensprache, zur AktivitĂ€tserkennung oder zum Unterrichten von Robotern. In der Vergangenheit wurden verschiedene AnsĂ€tze zur 3D-Handbewegungsrekonstruktion aktiv erforscht. Handschuhe und physische Markierungen sind zwar prĂ€zise, aber aufdringlich und unangenehm zu tragen. Daher ist eine markierungslose Handrekonstruktion auf der Basis von Kameras wĂŒnschenswert. Multi-Kamera-Setups bieten umfangreiche Eingabedaten, sind jedoch schwer zu kalibrieren und haben keine FlexibilitĂ€t fĂŒr mobile AnwendungsfĂ€lle. Daher verwenden die meisten neueren Methoden eine einzelne Farb- oder Tiefenkamera, was die Aufgabe jedoch schwerer macht, da mehr AmbiguitĂ€ten in den Eingabedaten vorhanden sind. FĂŒr Interaktionszwecke benötigen Benutzer kontinuierliche Kontrolle und sofortiges Feedback. Dies bedeutet, dass die Algorithmen in Echtzeit ausgefĂŒhrt werden mĂŒssen und robust in unkontrollierten Szenen sein mĂŒssen. Diese Anforderungen, 3D-Handrekonstruktion in Echtzeit mit einer einzigen Kamera in allgemeinen Szenen, machen das Problem erheblich schwieriger. WĂ€hrend neuere Forschungsarbeiten vielversprechende Ergebnisse gezeigt haben, weisen aktuelle Methoden immer noch EinschrĂ€nkungen auf. Die meisten AnsĂ€tze verfolgen die Bewegung einer einzelnen Hand nur isoliert und berĂŒcksichtigen keine alltĂ€glichen Umgebungen oder Interaktionen mit beliebigen Objekten oder der anderen Hand. Die wenigen Methoden, die allgemeinere und natĂŒrlichere Szenarien verarbeiten können, laufen nicht in Echtzeit oder verwenden komplexe Multi-Kamera-Setups. Solche Anforderungen machen bestehende Verfahren fĂŒr viele der oben genannten Anwendungen unbrauchbar. Diese Dissertation erweitert den Stand der Technik fĂŒr die Echtzeit-3D-Handverfolgung und -Rekonstruktion in allgemeinen Szenen mit einer einzelnen RGB- oder Tiefenkamera. Die vorgestellten Algorithmen erforschen neue Kombinationen aus generativen Handmodellen, die seit Jahrzehnten erfolgreich in den Bereichen Computer Vision und Grafik eingesetzt werden, und leistungsfĂ€higen innovativen Techniken des maschinellen Lernens, die vor kurzem mit dem Aufkommen neuronaler Netzwerke entstanden sind. In dieser Arbeit werden insbesondere vorgeschlagen: eine neuartige Methode zur Handbewegungsrekonstruktion bei starken Verdeckungen und in unkontrollierten Szenen, die erste Methode zur Rekonstruktion der globalen 3D Handbewegung aus RGB-Videos in freier Wildbahn und die erste Methode zur gleichzeitigen Rekonstruktion von Handpose und -form zweier interagierender HĂ€nde, die eine Reihe wĂŒnschenwerter Eigenschaften komibiniert
Human action recognition in stereoscopic videos based on bag of features and disparity pyramids
Publication in the conference proceedings of EUSIPCO, Lisbon, Portugal, 201
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking
With efficient appearance learning models, Discriminative Correlation Filter
(DCF) has been proven to be very successful in recent video object tracking
benchmarks and competitions. However, the existing DCF paradigm suffers from
two major issues, i.e., spatial boundary effect and temporal filter
degradation. To mitigate these challenges, we propose a new DCF-based tracking
method. The key innovations of the proposed method include adaptive spatial
feature selection and temporal consistent constraints, with which the new
tracker enables joint spatial-temporal filter learning in a lower dimensional
discriminative manifold. More specifically, we apply structured spatial
sparsity constraints to multi-channel filers. Consequently, the process of
learning spatial filters can be approximated by the lasso regularisation. To
encourage temporal consistency, the filter model is restricted to lie around
its historical value and updated locally to preserve the global structure in
the manifold. Last, a unified optimisation framework is proposed to jointly
select temporal consistency preserving spatial features and learn
discriminative filters with the augmented Lagrangian method. Qualitative and
quantitative evaluations have been conducted on a number of well-known
benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and
VOT2018. The experimental results demonstrate the superiority of the proposed
method over the state-of-the-art approaches
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
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