We investigate the unsupervised K-means clustering and the semi-supervised hidden Markov model (HMM) to automatically detect anomalous motion patterns in groups of people (crowds). Anomalous motion patterns are typically people merging into a dense group, followed by disturbances or threatening situations within the group. The application of K-means clustering and HMM are illustrated with datasets from four surveillance scenarios. The results indicate that by investigating the group of people in a systematic way with different K values, analyze cluster density, cluster quality and changes in cluster shape we can automatically detect anomalous motion patterns. The results correspond well with the events in the datasets. The results also indicate that very accurate detections of the people in the dense group would not be necessary. The clustering and HMM results will be very much the same also with some increased uncertainty in the detections. / <p>Funding Agencies|Vinnova (Swedish Governmental Agency for Innovation Systems) under the VINNMER program||</p>

In this thesis, we study the shape analysis problem and propose new methods for shape description, similarity and recognition. Firstly, we introduce a new shape descriptor in a two-step method. In the first step, the 2-D shape information is mapped into a set of 1-D functions. The mapping is based on the beams, which are originated from a boundary point, connecting that point with the rest of the points on the boundary. At each point, the angle between a pair of beams is taken as a random variable to define the statistics of the topological structure of the boundary. The third order statistics of all the beam angles is used to construct 1-D Beam Angle Statistics (BAS) functions. In the second step, we apply a set of feature extraction methods on BAS functions in order to describe it in a more compact form. BAS functions eliminate the context-dependency of the representation to the data set. BAS function is invariant to translation, rotation and scale. It is insensitive to distortions. No predefined resolution or threshold is required to define the BAS functions. Secondly, we adopt three different similarity distance methods defined on the BAS feature space, namely, Optimal Correspondence of String Subsequences, Dynamic Warping and Cyclic Sequence Matching algorithms. Main goal in these algorithms is to minimize the distance between two BAS features by allowing deformations. Thirdly, we propose a new Hidden Markov Model (HMM)topology for boundary based shape recognition. The proposed topology called Circular HMM is both ergodic and temporal. Therefore, the states can be revisited in finite time intervals while keeping the sequential information in the string, which represents the shape. It is insensitive to size changes. Since it has no starting and terminating state, it is insensitive to the starting point of the shape boundary. Experiments are done on the dataset of MPEG 7 Core Experiments Shape-1. It is observed that BAS descriptor outperforms all the methods in the literature. The Circular HMM gives higher recognition rates than the classical topologies in shape analysis applications.

Man portable air defence systems, MANPADS, pose a big threat to civilian and military aircraft. This thesis aims to find methods that could be used in a missile approach warning system based on infrared cameras. The two main tasks of the completed system are to classify the type of missile, and also to estimate its position and velocity from a sequence of images. The classification is based on hidden Markov models, one-class classifiers, and multi-class classifiers. Position and velocity estimation uses a model of the observed intensity as a function of real intensity, image coordinates, distance and missile orientation. The estimation is made by an extended Kalman filter. We show that fast classification of missiles based on radiometric data and a hidden Markov model is possible and works well, although more data would be needed to verify the results. Estimating the position and velocity works fairly well if the initial parameters are known. Unfortunately, some of these parameters can not be computed using the available sensor data.

<p>Hundreds of different factors adorn the eukaryotic genome, binding to it in large number. These DNA binding factors (DBFs) include nucleosomes, transcription factors (TFs), and other proteins and protein complexes, such as the origin recognition complex (ORC). DBFs compete with one another for binding along the genome, yet many current models of genome binding do not consider different types of DBFs together simultaneously. Additionally, binding is a stochastic process that results in a continuum of binding probabilities at any position along the genome, but many current models tend to consider positions as being either binding sites or not.</p><p>Here, we present a model that allows a multitude of DBFs, each at different concentrations, to compete with one another for binding sites along the genome. The result is an 'occupancy profile', a probabilistic description of the DNA occupancy of each factor at each position. We implement our model efficiently as the software package COMPETE. We demonstrate genome-wide and at specific loci how modeling nucleosome binding alters TF binding, and vice versa, and illustrate how factor concentration influences binding occupancy. Binding cooperativity between nearby TFs arises implicitly via mutual competition with nucleosomes. Our method applies not only to TFs, but also recapitulates known occupancy profiles of a well-studied replication origin with and without ORC binding.</p><p>We then develop a statistical framework for tuning our model concentrations to further improve its predictions. Importantly, this tuning optimizes with respect to actual biological data. We take steps to ensure that our tuned parameters are biologically plausible.</p><p>Finally, we discuss novel extensions and applications of our model, suggesting next steps in its development and deployment.</p> / Dissertation

Advanced process control solutions are oftentimes inadequate in their handling of uncertainty and disturbances. The main contribution of this work is to address this issue by providing solutions of immediate relevance to process control practitioners. To meet increasing performance demands, this work considers a Hidden Markov Model-based framework for describing non-stationary disturbance signals of practical interest such as intermittent drifts and abrupt jumps. The result is a more sophisticated model used by the state estimator for jump systems. At the expense of slightly higher computational costs (due to the state estimator), the proposed HMM disturbance model provides better tracking compared to a state estimator based on the commonly employed (in process control) integrated white noise disturbance model. Better tracking performance translates to superior closed loop performance without any redesign of the controller, through the typical assumption of separation and certainty equivalence. As a result, this provides a tool that can be readily adopted by process control practitioners. In line with this, the second aim is to develop approximate dynamic programming techniques for the rigorous control of nonlinear stochastic jump systems. The contribution is the creation of a framework that treats uncertainty in a systematic manner whilst leveraging existing off-the-shelf optimization solvers commonly employed by control practitioners.

<p>In this master's thesis, hidden Markov models (HMM) are evaluated as a tool for forecasting movements in a currency cross. With an ever increasing electronic market, making way for more automated trading, or so called algorithmic trading, there is constantly a need for new trading strategies trying to find alpha, the excess return, in the market.</p><p>HMMs are based on the well-known theories of Markov chains, but where the states are assumed hidden, governing some observable output. HMMs have mainly been used for speech recognition and communication systems, but have lately also been utilized on financial time series with encouraging results. Both discrete and continuous versions of the model will be tested, as well as single- and multivariate input data.</p><p>In addition to the basic framework, two extensions are implemented in the belief that they will further improve the prediction capabilities of the HMM. The first is a Gaussian mixture model (GMM), where one for each state assign a set of single Gaussians that are weighted together to replicate the density function of the stochastic process. This opens up for modeling non-normal distributions, which is often assumed for foreign exchange data. The second is an exponentially weighted expectation maximization (EWEM) algorithm, which takes time attenuation in consideration when re-estimating the parameters of the model. This allows for keeping old trends in mind while more recent patterns at the same time are given more attention.</p><p>Empirical results shows that the HMM using continuous emission probabilities can, for some model settings, generate acceptable returns with Sharpe ratios well over one, whilst the discrete in general performs poorly. The GMM therefore seems to be an highly needed complement to the HMM for functionality. The EWEM however does not improve results as one might have expected. Our general impression is that the predictor using HMMs that we have developed and tested is too unstable to be taken in as a trading tool on foreign exchange data, with too many factors influencing the results. More research and development is called for.</p>

Magistro darbe pristatoma sukurta programa, realizuojanti interneto naršyklės valdymą balsu. Ši programa papildo atskirų žodžių prototipinę atpažinimo sistemą, pagrįstą paslėptaisiais Markovo modeliais (PMM). Šios dvi dalys ir sudaro interneto naršyklės valdymo balsu prototipą, kuris gali atpažinti 71 komandą (vienas arba du žodžiai) lietuvių kalba: 1 komandą, skirtą naršyklės atvėrimui, 54 naršyklės valdymo komandas, 16 komandų, atveriančių konkrečius iš anksto sistemai nurodytus tinklalapius. Darbe aprašytas lietuvių kalbos atskirų žodžių atpažinimo sistemos akustinių modelių, grįstų paslėptaisiais Markovo modeliais, rinkinių eksperimentinis tyrimas. Atsižvelgiant į įvairius atpažinimui turinčius įtakos veiksnius (mokymo duomenų kiekį, mišinio komponenčių skaičių, kalbėtojo lytį, skirtingos techninės įrangos naudojimą atpažinime), buvo sukurti skirtingi balso komandų akustinių modelių rinkiniai. Eksperimentinio tyrimo metu buvo tiriama šių rinkinių panaudojimo atpažinimo sistemoje įtaka sistemos atpažinimo tikslumui. Eksperimentinio tyrimo rezultatai parodė, kad interneto naršyklės valdymo balsu sistemos prototipo atpažinimo tikslumas siekia 98%. Sistema gali būti naudojama kaip vaizdinė priemonė vyresniųjų klasių moksleiviams informacinių technologijų, fizikos, psichologijos, matematikos pamokose. / The thesis presents a prototype of the software (system) for Web browser control by voice. The prototype consists of two parts: the Hidden Markov Models based word recognition system and the program, which implements browser control by voice commands and is integrated in the word recognition system. The prototype is a speaker-independent Lithuanian word (voice commands) recognition system and can recognize 71 voice commands: 1 command is intended to run browser, 54 commands – for browser control, and 16 commands – to open various user predefined websites. Taking into account various factors (amount of training data, number of Gaussian mixture components, gender of speaker, use of different hardware for recognition) which have impact on recognition, different sets of acoustic models of Lithuanian voice commands were created and trained. An experimental investigation of the influence of the sets usage in Lithuanian word recognition system on the word recognition accuracy was performed. The results of the experimental investigation showed that created prototype system achieves 98% word recognition accuracy. The prototype system can be used at secondary school as a visual speech recognition learning tool in the informatics, physics, psychology, and mathematics lessons for the pupils of senior classes.

Estimation of the number of hidden states is challenging in hidden Markov models. Motivated by the analysis of a specific type of cell adhesion experiments, a new frame-work based on hidden Markov model and double penalized order selection is proposed. The order selection procedure is shown to be consistent in estimating the number of states. A modified Expectation-Maximization algorithm is introduced to efficiently estimate parameters in the model. Simulations show that the proposed framework outperforms existing methods. Applications of the proposed methodology to real data demonstrate the accuracy of estimating receptor-ligand bond lifetimes and waiting times which are essential in kinetic parameter estimation. The second part of the thesis is concerned with prediction of a deterministic response function y at some untried sites given values of y at a chosen set of design sites. The intended application is to computer experiments in which y is the output from a computer simulation and each design site represents a particular configuration of the input variables. A Bayesian version of the cubic spline method commonly used in numerical analysis is proposed, in which the random function that represents prior uncertainty about y is taken to be a specific stationary Gaussian process. An MCMC procedure is given for updating the prior given the observed y values. Simulation examples and a real data application are given to compare the performance of the Bayesian cubic spline with that of two existing methods.

This thesis is devoted to the problems of defining and developing the basic building blocks of an automated surveillance system. As its initial step, a background-modeling algorithm is described for segmenting moving objects from the background, which is capable of adapting to dynamic scene conditions, as well as determining shadows of the moving objects. After obtaining binary silhouettes for targets, object association between consecutive frames is achieved by a hypothesis-based tracking method. Both of these tasks provide basic information for higher-level processing, such as activity analysis and object identification. In order to recognize the nature of an event occurring in a scene, hidden Markov models (HMM) are utilized. For this aim, object trajectories, which are obtained through a successful track, are written as a sequence of flow vectors that capture the details of instantaneous velocity and location information. HMMs are trained with sequences obtained from usual motion patterns and abnormality is detected by measuring the distance to these models. Finally, MPEG-7 visual descriptors are utilized in a regional manner for object identification. Color structure and homogeneous texture parameters of the independently moving objects are extracted and classifiers, such as Support Vector Machine (SVM) and Bayesian plug-in (Mahalanobis distance), are utilized to test the performance of the proposed person identification mechanism. The simulation results with all the above building blocks give promising results, indicating the possibility of constructing a fully automated surveillance system for the future.

In this study, a new discriminative learning framework, called soft margin estimation (SME), is proposed for estimating the parameters of continuous density hidden Markov models (HMMs). The proposed method makes direct use of the successful ideas of margin in support vector machines to improve generalization capability and decision feedback learning in discriminative training to enhance model separation in classifier design. SME directly maximizes the separation of competing models to enhance the testing samples to approach a correct decision if the deviation from training samples is within a safe margin. Frame and utterance selections are integrated into a unified framework to select the training utterances and frames critical for discriminating competing models. SME offers a flexible and rigorous framework to facilitate the incorporation of new margin-based optimization criteria into HMMs training. The choice of various loss functions is illustrated and different kinds of separation measures are defined under a unified SME framework. SME is also shown to be able to jointly optimize feature extraction and HMMs. Both the generalized probabilistic descent algorithm and the Extended Baum-Welch algorithm are applied to solve SME. SME has demonstrated its great advantage over other discriminative training methods in several speech recognition tasks. Tested on the TIDIGITS digit recognition task, the proposed SME approach achieves a string accuracy of 99.61%, the best result ever reported in literature. On the 5k-word Wall Street Journal task, SME reduced the word error rate (WER) from 5.06% of MLE models to 3.81%, with relative 25% WER reduction. This is the first attempt to show the effectiveness of margin-based acoustic modeling for large vocabulary continuous speech recognition in a HMMs framework. The generalization of SME was also well demonstrated on the Aurora 2 robust speech recognition task, with around 30% relative WER reduction from the clean-trained baseline.