Anomalous event detection using a semi-two dimensional hidden Markov model

The rapid increase in the deployment of CCTV systems has led to a greater demand for algorithms that are able to process incoming video feeds. These algorithms are designed to extract information of interest for human operators. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned `normal' model. Many researchers have tried various sets of features to train different learning models to detect abnormal behaviour in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM) to model the normal activities of people. The outliers of the model with insufficient likelihood are identified as abnormal activities. Our Semi-2D HMM is designed to model both the temporal and spatial causalities of the crowd behaviour by assuming the current state of the Hidden Markov Model depends not only on the previous state in the temporal direction, but also on the previous states of the adjacent spatial locations. Two different HMMs are trained to model both the vertical and horizontal spatial causal information. Location features, flow features and optical flow textures are used as the features for the model. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Anomalous Seebeck coefficient in boron carbides

Boron carbides exhibit an anomalously large Seebeck coefficient with a temperature coefficient that is characteristic of polaronic hopping between inequivalent sites. The inequivalence in the sites is associated with disorder in the solid. The temperature dependence of the Seebeck coefficient for materials prepared by different techniques provides insight into the nature of the disorder.

Evaluating the role of deep granitic rocks in generating anomalous temperatures in SW-Queensland

Across central Australia and south-west Queensland, a large (~800,000km2) subsurface temperature anomaly occurs (Figure 1). Temperatures are interpreted to be greater than 235°C at 5km depth, ca. 85°C higher than the average geothermal gradient for the upper continental crust (Chopra & Holgate, 2005; Holgate & Gerner, 2011). This anomaly has driven the development of Engineered Geothermal Systems (EGS) at Innamincka, where high temperatures have been related to the radiogenic heat production of High Heat Producing Granites (HHPG) at depth, below thermally insulative sedimentary cover (Chopra & Holgate, 2005; Draper & D’Arcy, 2006; Meixner & Holgate, 2009). To evaluate the role of granitic rocks at depth in generating the broader temperature anomaly in SW-Queensland, we sampled 25 granitic rocks from basement intervals of petroleum drill cores below thermal insulative cover along two transects (WNW–ESE and NNE–SSW — Figure 1) and performed a multidisciplinary study involving petrography, whole-rock chemistry, zircon dating and thermal conductivity measurements.

Numerical simulation of anomalous diffusion with application to medical imaging

The first objective of this project is to develop new efficient numerical methods and supporting error and convergence analysis for solving fractional partial differential equations to study anomalous diffusion in biological tissue such as the human brain. The second objective is to develop a new efficient fractional differential-based approach for texture enhancement in image processing. The results of the thesis highlight that the fractional order analysis captured important features of nuclear magnetic resonance (NMR) relaxation and can be used to improve the quality of medical imaging.

Detecting anomalous events at railway level crossings

Collisions between pedestrians and vehicles continue to be a major problem throughout the world. Pedestrians trying to cross roads and railway tracks without any caution are often highly susceptible to collisions with vehicles and trains. Continuous financial, human and other losses have prompted transport related organizations to come up with various solutions addressing this issue. However, the quest for new and significant improvements in this area is still ongoing. This work addresses this issue by building a general framework using computer vision techniques to automatically monitor pedestrian movements in such high-risk areas to enable better analysis of activity, and the creation of future alerting strategies. As a result of rapid development in the electronics and semi-conductor industry there is extensive deployment of CCTV cameras in public places to capture video footage. This footage can then be used to analyse crowd activities in those particular places. This work seeks to identify the abnormal behaviour of individuals in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM), Full-2D HMM and Spatial HMM to model the normal activities of people. The outliers of the model (i.e. those observations with insufficient likelihood) are identified as abnormal activities. Location features, flow features and optical flow textures are used as the features for the model. The proposed approaches are evaluated using the publicly available UCSD datasets, and we demonstrate improved performance using a Semi-2D Hidden Markov Model compared to other state of the art methods. Further we illustrate how our proposed methods can be applied to detect anomalous events at rail level crossings.

An evaluation of different features and learning models for anomalous event detection

The huge amount of CCTV footage available makes it very burdensome to process these videos manually through human operators. This has made automated processing of video footage through computer vision technologies necessary. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned ‘normal’ model. There is no precise and exact definition for an abnormal activity; it is dependent on the context of the scene. Hence there is a requirement for different feature sets to detect different kinds of abnormal activities. In this work we evaluate the performance of different state of the art features to detect the presence of the abnormal objects in the scene. These include optical flow vectors to detect motion related anomalies, textures of optical flow and image textures to detect the presence of abnormal objects. These extracted features in different combinations are modeled using different state of the art models such as Gaussian mixture model(GMM) and Semi- 2D Hidden Markov model(HMM) to analyse the performances. Further we apply perspective normalization to the extracted features to compensate for perspective distortion due to the distance between the camera and objects of consideration. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Finite volume methods for simulating anomalous transport

In this thesis a new approach for solving a certain class of anomalous diffusion equations was developed. The theory and algorithms arising from this work will pave the way for more efficient and more accurate solutions of these equations, with applications to science, health and industry. The method of finite volumes was applied to discretise the spatial derivatives, and this was shown to outperform existing methods in several key respects. The stability and convergence of the new method were rigorously established.

Numerical simulation of anomalous infiltration in porous media

Nonlinear time-fractional diffusion equations have been used to describe the liquid infiltration for both subdiffusion and superdiffusion in porous media. In this paper, some problems of anomalous infiltration with a variable-order timefractional derivative in porous media are considered. The time-fractional Boussinesq equation is also considered. Two computationally efficient implicit numerical schemes for the diffusion and wave-diffusion equations are proposed. Numerical examples are provided to show that the numerical methods are computationally efficient.

Characterization of anomalous relaxation using the time-fractional Bloch equation and multiple echo T2 *-weighted magnetic resonance imaging at 7T

PURPOSE To study the utility of fractional calculus in modeling gradient-recalled echo MRI signal decay in the normal human brain. METHODS We solved analytically the extended time-fractional Bloch equations resulting in five model parameters, namely, the amplitude, relaxation rate, order of the time-fractional derivative, frequency shift, and constant offset. Voxel-level temporal fitting of the MRI signal was performed using the classical monoexponential model, a previously developed anomalous relaxation model, and using our extended time-fractional relaxation model. Nine brain regions segmented from multiple echo gradient-recalled echo 7 Tesla MRI data acquired from five participants were then used to investigate the characteristics of the extended time-fractional model parameters. RESULTS We found that the extended time-fractional model is able to fit the experimental data with smaller mean squared error than the classical monoexponential relaxation model and the anomalous relaxation model, which do not account for frequency shift. CONCLUSIONS We were able to fit multiple echo time MRI data with high accuracy using the developed model. Parameters of the model likely capture information on microstructural and susceptibility-induced changes in the human brain.

Numerical approximation of a fractional-in-space diffusion equation (II) - with nonhomogeneous boundary conditions

In this paper, a space fractional di®usion equation (SFDE) with non- homogeneous boundary conditions on a bounded domain is considered. A new matrix transfer technique (MTT) for solving the SFDE is proposed. The method is based on a matrix representation of the fractional-in-space operator and the novelty of this approach is that a standard discretisation of the operator leads to a system of linear ODEs with the matrix raised to the same fractional power. Analytic solutions of the SFDE are derived. Finally, some numerical results are given to demonstrate that the MTT is a computationally e±cient and accurate method for solving SFDE.

Polymer nanocomposites based on P3OT, TPU and SWNT: preparation and characterization

Single walled carbon nanotubes (SWNTs) were incorporated in polymer nanocomposites based on poly(3-octylthiophene) (P3OT), thermoplastic polyurethane (TPU) or a blend of them. Thermogravimetry demonstrated the success of the purification procedure employed in the chemical treatment of SWNTs prior to composite preparation. Stable dispersions of SWNTs in chloroform were obtained by non-covalent interactions with the dissolved polymers. Composites exhibited glass transitions, melting temperatures and heat of fusion which changed in relation to pure polymers. This behavior is discussed as associated to interactions between nanotubes and polymers. The conductivity at room temperature of the blend (TPU-P3OT) with SWNT is higher than the P3OT/SWNT composite.

Micro-structure differences in kaolinite suspensions

SEM observations of the aqueous suspensions of kaolinite from Birdwood (South Australia) and Georgia (USA) show noticeable differences in number of physical behaviour which has been explained by different microstructure constitution.. Birdwood kaolinite dispersion gels are observed at very low solid loadings in comparison with Georgia KGa-1 kaolinite dispersions which remain fluid at higher solids loading. To explain this behaviour, the specific particle interactions of Birdwood kaolinite, different from interaction in Georgia kaolinite have been proposed. These interactions may be brought about by the presence of nano-bubbles on clay crystal edges and may force clay particles to aggregate by bubble coalescence. This explains the predominance of stair step edge-edge like (EE) contacts in suspension of Birdwood kaolinite. Such EE linked particles build long strings that form a spacious cell structure. Hydrocarbon contamination of colloidal kaolinite particles and low aspect ratio are discussed as possible explanations of this unusual behaviour of Birdwood kaolinite. In Georgia KGa-1 kaolinite dispersions instead of EE contact between platelets displayed in Birdwood kaolinite, most particles have edge to face (EF) contacts building a cardhouse structure. Such an arrangement is much less voluminous in comparison with the Birdwood kaolinite cellular honeycomb structure observed previously in smectite aqueous suspensions. Such structural characteristics of KGa-1 kaolinite particles enable higher solid volume fractions pulps to form before significantly networked gel consistency is attained.