144 research outputs found
Online learning and fusion of orientation appearance models for robust rigid object tracking
We present a robust framework for learning and fusing different modalities for rigid object tracking. Our method fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depths cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically, our method combines image gradient orientations as extracted from intensity images with the directions of surface normal computed from dense depth fields provided by the Kinect. To incorporate these features in a learning framework, we use a robust kernel based on the Euler representation of angles. This kernel enables us to cope with gross measurement errors, missing data as well as typical problems in visual tracking such as illumination changes and occlusions. Additionally, the employed kernel can be efficiently implemented online. Finally, we propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original AAM. Thus the proposed learning and fusing framework is robust, exact, computationally efficient and does not require off-line training. By combining the proposed models with a particle filter, the proposed tracking framework achieved robust performance in very difficult tracking scenarios including extreme pose variations
Investigations on number selection for finite mixture models and clustering analysis.
by Yiu Ming Cheung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 92-99).Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.1.1 --- Bayesian YING-YANG Learning Theory and Number Selec- tion Criterion --- p.5Chapter 1.2 --- General Motivation --- p.6Chapter 1.3 --- Contributions of the Thesis --- p.6Chapter 1.4 --- Other Related Contributions --- p.7Chapter 1.4.1 --- A Fast Number Detection Approach --- p.7Chapter 1.4.2 --- Application of RPCL to Prediction Models for Time Series Forecasting --- p.7Chapter 1.4.3 --- Publications --- p.8Chapter 1.5 --- Outline of the Thesis --- p.8Chapter 2 --- Open Problem: How Many Clusters? --- p.11Chapter 3 --- Bayesian YING-YANG Learning Theory: Review and Experiments --- p.17Chapter 3.1 --- Briefly Review of Bayesian YING-YANG Learning Theory --- p.18Chapter 3.2 --- Number Selection Criterion --- p.20Chapter 3.3 --- Experiments --- p.23Chapter 3.3.1 --- Experimental Purposes and Data Sets --- p.23Chapter 3.3.2 --- Experimental Results --- p.23Chapter 4 --- Conditions of Number Selection Criterion --- p.39Chapter 4.1 --- Alternative Condition of Number Selection Criterion --- p.40Chapter 4.2 --- Conditions of Special Hard-cut Criterion --- p.45Chapter 4.2.1 --- Criterion Conditions in Two-Gaussian Case --- p.45Chapter 4.2.2 --- Criterion Conditions in k*-Gaussian Case --- p.59Chapter 4.3 --- Experimental Results --- p.60Chapter 4.3.1 --- Purpose and Data Sets --- p.60Chapter 4.3.2 --- Experimental Results --- p.63Chapter 4.4 --- Discussion --- p.63Chapter 5 --- Application of Number Selection Criterion to Data Classification --- p.80Chapter 5.1 --- Unsupervised Classification --- p.80Chapter 5.1.1 --- Experiments --- p.81Chapter 5.2 --- Supervised Classification --- p.82Chapter 5.2.1 --- RBF Network --- p.85Chapter 5.2.2 --- Experiments --- p.86Chapter 6 --- Conclusion and Future Work --- p.89Chapter 6.1 --- Conclusion --- p.89Chapter 6.2 --- Future Work --- p.90Bibliography --- p.92Chapter A --- A Number Detection Approach for Equal-and-Isotropic Variance Clusters --- p.100Chapter A.1 --- Number Detection Approach --- p.100Chapter A.2 --- Demonstration Experiments --- p.102Chapter A.3 --- Remarks --- p.105Chapter B --- RBF Network with RPCL Approach --- p.106Chapter B.l --- Introduction --- p.106Chapter B.2 --- Normalized RBF net and Extended Normalized RBF Net --- p.108Chapter B.3 --- Demonstration --- p.110Chapter B.4 --- Remarks --- p.113Chapter C --- Adaptive RPCL-CLP Model for Financial Forecasting --- p.114Chapter C.1 --- Introduction --- p.114Chapter C.2 --- Extraction of Input Patterns and Outputs --- p.115Chapter C.3 --- RPCL-CLP Model --- p.116Chapter C.3.1 --- RPCL-CLP Architecture --- p.116Chapter C.3.2 --- Training Stage of RPCL-CLP --- p.117Chapter C.3.3 --- Prediction Stage of RPCL-CLP --- p.122Chapter C.4 --- Adaptive RPCL-CLP Model --- p.122Chapter C.4.1 --- Data Pre-and-Post Processing --- p.122Chapter C.4.2 --- Architecture and Implementation --- p.122Chapter C.5 --- Computer Experiments --- p.125Chapter C.5.1 --- Data Sets and Experimental Purpose --- p.125Chapter C.5.2 --- Experimental Results --- p.126Chapter C.6 --- Conclusion --- p.134Chapter D --- Publication List --- p.135Chapter D.1 --- Publication List --- p.13
Online learning and fusion of orientation appearance models for robust rigid object tracking
We introduce a robust framework for learning and fusing of orientation appearance models based on both texture and depth information for rigid object tracking. Our framework fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depth cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically, our framework combines image gradient orientations as extracted from intensity images with the directions of surface normals computed from dense depth fields. We propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original Active Appearance Models (AAMs). To incorporate these features in a learning framework, we use a robust kernel based on the Euler representation of angles which does not require off-line training, and can be efficiently implemented online. The robustness of learning from orientation appearance models is presented both theoretically and experimentally in this work. This kernel enables us to cope with gross measurement errors, missing data as well as other typical problems such as illumination changes and occlusions. By combining the proposed models with a particle filter, the proposed framework was used for performing 2D plus 3D rigid object tracking, achieving robust performance in very difficult tracking scenarios including extreme pose variations. © 2014 Elsevier B.V. All rights reserved
Real-time appearance-based gaze tracking.
PhDGaze tracking technology is widely used in Human Computer Interaction applications
such as in interfaces for assisting people with disabilities and for driver attention monitoring.
However, commercially available gaze trackers are expensive and their performance
deteriorates if the user is not positioned in front of the camera and facing it. Also, head
motion or being far from the device degrades their accuracy.
This thesis focuses on the development of real-time time appearance based gaze
tracking algorithms using low cost devices, such as a webcam or Kinect. The proposed
algorithms are developed by considering accuracy, robustness to head pose variation and
the ability to generalise to different persons. In order to deal with head pose variation, we
propose to estimate the head pose and then compensate for the appearance change and
the bias to a gaze estimator that it introduces. Head pose is estimated by a novel method
that utilizes tensor-based regressors at the leaf nodes of a random forest. For a baseline
gaze estimator we use an SVM-based appearance-based regressor. For compensating
the appearance variation introduced by the head pose, we use a geometric model, and
for compensating for the bias we use a regression function that has been trained on a
training set. Our methods are evaluated on publicly available dataset
On the subspace learning for network attack detection
Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2019.O custo com todos os tipos de ciberataques tem crescido nas organizações. A casa branca do
goveno norte americano estima que atividades cibernéticas maliciosas custaram em 2016 um
valor entre US109 bilhões para a economia norte americana. Recentemente, é
possível observar um crescimento no número de ataques de negação de serviço, botnets,
invasões e ransomware.
A Accenture argumenta que 89% dos entrevistados em uma pesquisa acreditam que tecnologias
como inteligência artificial, aprendizagem de máquina e análise baseada em comportamentos,
são essenciais para a segurança das organizações. É possível adotar abordagens semisupervisionada e não-supervisionadas para implementar análises baseadas em
comportamentos, que podem ser aplicadas na detecção de anomalias em tráfego de rede, sem a
ncessidade de dados de ataques para treinamento.
Esquemas de processamento de sinais têm sido aplicados na detecção de tráfegos maliciosos
em redes de computadores, através de abordagens não-supervisionadas que mostram ganhos
na detecção de ataques de rede e na detecção e anomalias.
A detecção de anomalias pode ser desafiadora em cenários de dados desbalanceados, que são
casos com raras ocorrências de anomalias em comparação com o número de eventos normais.
O desbalanceamento entre classes pode comprometer o desempenho de algoritmos traficionais
de classificação, através de um viés para a classe predominante, motivando o desenvolvimento
de algoritmos para detecção de anomalias em dados desbalanceados.
Alguns algoritmos amplamente utilizados na detecção de anomalias assumem que observações
legítimas seguem uma distribuição Gaussiana. Entretanto, esta suposição pode não ser
observada na análise de tráfego de rede, que tem suas variáveis usualmente caracterizadas por
distribuições assimétricas
ou de cauda pesada. Desta forma, algoritmos de detecção de anomalias têm atraído pesquisas
para se tornarem mais discriminativos em distribuições assimétricas, como também para se
tornarem mais robustos à corrupção e capazes de lidar com problemas causados pelo
desbalanceamento de dados.
Como uma primeira contribuição, foi proposta a Autosimilaridade (Eigensimilarity em inglês), que
é uma abordagem baseada em conceitos de processamento de sinais com o objetivo de detectar
tráfego malicioso em redes de computadores. Foi avaliada a acurácia e o desempenho da
abordagem proposta através de cenários simulados e dos dados do DARPA 1998. Os
experimentos mostram que Autosimilaridade detecta os ataques synflood, fraggle e varredura de
portas com precisão, com detalhes e de uma forma automática e cega, i.e. em uma abordagem
não-supervisionada.
Considerando que a assimetria de distribuições de dados podem melhorar a detecção de
anomalias em dados desbalanceados e assimétricos, como no caso de tráfego de rede, foi
proposta a Análise Robusta de Componentes Principais baseada em Momentos (ARCP-m), que
é uma abordagem baseada em distâncias entre observações contaminadas e momentos
calculados a partir subespaços robustos aprendidos através da Análise Robusta de
Componentes Principais (ARCP), com o objetivo de detectar anomalias em dados assimétricos e
em tráfego de rede.
Foi avaliada a acurácia do ARCP-m para detecção de anomalias em dados simulados, com
distribuições assimétricas e de cauda pesada, como também para os dados do CTU-13. Os
experimentos comparam nossa proposta com algoritmos amplamente utilizados para detecção
de anomalias e mostra que a distância entre estimativas robustas e observações contaminadas
pode melhorar a detecção de anomalias em dados assimétricos e a detecção de ataques de
rede.
Adicionalmente, foi proposta uma arquitetura e abordagem para avaliar uma prova de conceito
da Autosimilaridade para a detecção de comportamentos maliciosos em aplicações móveis
corporativas. Neste sentido, foram propostos cenários, variáveis e abordagem para a análise de
ameaças, como também foi avaliado o tempo de processamento necessário para a execução do
Autosimilaridade em dispositivos móveis.The cost of all types of cyberattacks is increasing for global organizations. The Whitehouse of the
U.S. government estimates that malicious cyber activity cost the U.S. economy between US109 billion in 2016. Recently, it is possible to observe an increasing in numbers of
Denial of Service (DoS), botnets, malicious insider and ransomware attacks.
Accenture consulting argues that 89% of survey respondents believe breakthrough technologies,
like artificial intelligence, machine learning and user behavior analytics, are essential for securing
their organizations. To face adversarial models, novel network attacks and counter measures of
attackers to avoid detection, it is possible to adopt unsupervised or semi-supervised approaches
for network anomaly detection, by means of behavioral analysis, where known anomalies are not
necessaries for training models.
Signal processing schemes have been applied to detect malicious traffic in computer networks
through unsupervised approaches, showing advances in network traffic analysis, in network
attack detection, and in network intrusion detection systems.
Anomalies can be hard to identify and separate from normal data due to the rare occurrences of
anomalies in comparison to normal events. The imbalanced data can compromise the
performance of most standard learning algorithms, creating bias or unfair weight to learn from the
majority class and reducing detection capacity of anomalies that are characterized by the minority
class. Therefore, anomaly detection algorithms have to be highly discriminating, robust to
corruption and able to deal with the imbalanced data problem.
Some widely adopted algorithms for anomaly detection assume a Gaussian distributed data for
legitimate observations, however this assumption may not be observed in network traffic, which is
usually characterized by skewed and heavy-tailed distributions.
As a first important contribution, we propose the Eigensimilarity, which is an approach based on
signal processing concepts applied to detection of malicious traffic in computer networks. We
evaluate the accuracy and performance of the proposed framework applied to a simulated
scenario and to the DARPA 1998 data set. The performed experiments show that synflood,
fraggle and port scan attacks can be detected accurately by Eigensimilarity and with great detail,
in an automatic and blind fashion, i.e. in an unsupervised approach.
Considering that the skewness improves anomaly detection in imbalanced and skewed data,
such as network traffic, we propose the Moment-based Robust Principal Component Analysis (mRPCA) for network attack detection. The m-RPCA is a framework based on distances between
contaminated observations and moments computed from a robust subspace learned by Robust
Principal Component Analysis (RPCA), in order to detect anomalies from skewed data and
network traffic. We evaluate the accuracy of the m-RPCA for anomaly detection on simulated
data sets, with skewed and heavy-tailed distributions, and for the CTU-13 data set. The
Experimental evaluation compares our proposal to widely adopted algorithms for anomaly
detection and shows that the distance between robust estimates and contaminated observations
can improve the anomaly detection on skewed data and the network attack detection.
Moreover, we propose an architecture and approach to evaluate a proof of concept of
Eigensimilarity for malicious behavior detection on mobile applications, in order to detect possible
threats in offline corporate mobile client. We propose scenarios, features and approaches for
threat analysis by means of Eigensimilarity, and evaluate the processing time required for
Eigensimilarity execution in mobile devices
Learning classifier systems from first principles: A probabilistic reformulation of learning classifier systems from the perspective of machine learning
Learning Classifier Systems (LCS) are a family of rule-based machine learning methods. They aim at the autonomous production of potentially human readable results that are the most compact generalised representation whilst also maintaining high predictive accuracy, with a wide range of application areas, such as autonomous robotics, economics, and multi-agent systems. Their design is mainly approached heuristically and, even though their performance is competitive in regression and classification tasks, they do not meet their expected performance in sequential decision tasks despite being initially designed for such tasks. It is out contention that improvement is hindered by a lack of theoretical understanding of their underlying mechanisms and dynamics.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Single to multiple target, multiple type visual tracking
Visual tracking is a key task in applications such as intelligent surveillance, humancomputer interaction (HCI), human-robot interaction (HRI), augmented reality (AR), driver assistance systems, and medical applications. In this thesis, we make three main novel contributions for target tracking in video sequences.
First, we develop a long-term model-free single target tracking by learning discriminative correlation filters and an online classifier that can track a target of interest in both sparse and crowded scenes. In this case, we learn two different correlation filters, translation and scale correlation filters, using different visual features. We also include a re-detection module that can re-initialize the tracker in case of tracking failures due to long-term occlusions.
Second, a multiple target, multiple type filtering algorithm is developed using Random Finite Set (RFS) theory. In particular, we extend the standard Probability Hypothesis Density (PHD) filter for multiple type of targets, each with distinct detection properties, to develop multiple target, multiple type filtering, N-type PHD filter, where N ≥ 2, for handling confusions that can occur among target types at the measurements level. This method takes into account not only background false positives (clutter), but also confusions between target detections, which are in general different in character from background clutter. Then, under the assumptions of Gaussianity and linearity, we extend Gaussian mixture (GM) implementation of the standard PHD filter for the proposed N-type PHD filter termed as N-type GM-PHD filter.
Third, we apply this N-type GM-PHD filter to real video sequences by integrating object detectors’ information into this filter for two scenarios. In the first scenario, a tri-GM-PHD filter is applied to real video sequences containing three types of multiple targets in the same scene, two football teams and a referee, using separate but confused detections. In the second scenario, we use a dual GM-PHD filter for tracking pedestrians and vehicles in the same scene handling their detectors’ confusions. For both cases, Munkres’s variant of the Hungarian assignment algorithm is used to associate tracked target identities between frames.
We make extensive evaluations of these developed algorithms and find out that our methods outperform their corresponding state-of-the-art approaches by a large margin.EPSR
Investigation of the impact of high frequency transmitted speech on speaker recognition
Thesis (MScEng)--Stellenbosch University, 2002.Some digitised pages may appear illegible due to the condition of the original hard copy.ENGLISH ABSTRACT: Speaker recognition systems have evolved to a point where near perfect performance can be
obtained under ideal conditions, even if the system must distinguish between a large number
of speakers. Under adverse conditions, such as when high noise levels are present or when the
transmission channel deforms the speech, the performance is often less than satisfying.
This project investigated the performance of a popular speaker recognition system, that use
Gaussian mixture models, on speech transmitted over a high frequency channel. Initial experiments
demonstrated very unsatisfactory results for the base line system.
We investigated a number of robust techniques. We implemented and applied some of them in
an attempt to improve the performance of the speaker recognition systems. The techniques we
tested showed only slight improvements.
We also investigates the effects of a high frequency channel and single sideband modulation on
the speech features of speech processing systems. The effects that can deform the features, and
therefore reduce the performance of speech systems, were identified.
One of the effects that can greatly affect the performance of a speech processing system is
noise. We investigated some speech enhancement techniques and as a result we developed a
new statistical based speech enhancement technique that employs hidden Markov models to
represent the clean speech process.AFRIKAANSE OPSOMMING: Sprekerherkenning-stelsels het 'n punt bereik waar nabyaan perfekte resultate verwag kan word
onder ideale kondisies, selfs al moet die stelsel tussen 'n groot aantal sprekers onderskei. Wanneer
nie-ideale kondisies, soos byvoorbeeld hoë ruisvlakke of 'n transmissie kanaal wat die
spraak vervorm, teenwoordig is, is die resultate gewoonlik nie bevredigend nie.
Die projek ondersoek die werksverrigting van 'n gewilde sprekerherkenning-stelsel, wat gebruik
maak van Gaussiese mengselmodelle, op spraak wat oor 'n hoë frekwensie transmissie
kanaal gestuur is. Aanvanklike eksperimente wat gebruik maak van 'n basiese stelsel het nie
goeie resultate opgelewer nie.
Ons het 'n aantal robuuste tegnieke ondersoek en 'n paar van hulle geïmplementeer en getoets
in 'n poging om die resultate van die sprekerherkenning-stelsel te verbeter. Die tegnieke wat
ons getoets het, het net geringe verbetering getoon.
Die studie het ook die effekte wat die hoë-frekwensie kanaal en enkel-syband modulasie op
spraak kenmerkvektore, ondersoek. Die effekte wat die spraak kenmerkvektore kan vervorm en
dus die werkverrigting van spraak stelsels kan verlaag, is geïdentifiseer.
Een van die effekte wat 'n groot invloed op die werkverrigting van spraakstelsels het, is ruis.
Ons het spraak verbeterings metodes ondersoek en dit het gelei tot die ontwikkeling van 'n
statisties gebaseerde spraak verbeteringstegniek wat gebruik maak van verskuilde Markov modelle
om die skoon spraakproses voor te stel
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