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.

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.

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.

Stress Relaxation for Granular Materials near Jamming under Cyclic Compression.

We have explored isotropically jammed states of semi-2D granular materials through cyclic compression. In each compression cycle, systems of either identical ellipses or bidisperse disks transition between jammed and unjammed states. We determine the evolution of the average pressure P and structure through consecutive jammed states. We observe a transition point ϕ_{m} above which P persists over many cycles; below ϕ_{m}, P relaxes slowly. The relaxation time scale associated with P increases with packing fraction, while the relaxation time scale for collective particle motion remains constant. The collective motion of the ellipses is hindered compared to disks because of the rotational constraints on elliptical particles.

Coordination of UAV path planning in 2D and 3D in environmental/bio sensing applications

This paper is concerned with the optimal path planning and initialization interval of one or two UAVs in presence of a constant wind. The method compares previous literature results on synchronization of UAVs along convex curves, path planning and sampling in 2D and extends it to 3D. This method can be applied to observe gas/particle emissions inside a control volume during sampling loops. The flight pattern is composed of two phases: a start-up interval and a sampling interval which is represented by a semi-circular path. The methods were tested in four complex model test cases in 2D and 3D as well as one simulated real world scenario in 2D and one in 3D.

Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

Main chain and segmental dynamics of semi interpenetrating based on polyisoprene and poly(methyl methacrylate)

Main chain and segmental dynamics of polyisoprene (PI) and poly(methyl methacrylate)(PMMA) chains in semi IPNs were systematically studied over a wide range of temperatures (above and below T-g of both polymers) as a function of composition, crosslink density, and molecular weight. The immiscible polymers retained most of its characteristic molecular motion; however, the semi IPN synthesis resulted in dramatic changes in the motional behavior of both polymers due to the molecular level interpenetration between two polymer chains. ESR spin probe method was found to be sensitive to the concentration changes of PMMA in semi IPNs. Low temperature spectra showed the characteristics of rigid limit spectra, and in the range of 293-373 K.complex spectra were obtained with the slow component mostly arisingout of the PMMA rich regions and fast component from the PI phase. We found that the rigid PMMA chains closely interpenetrated into thehighly mobile PI network imparts motional restriction in nearby PI chains, and the highly mobile PI chains induce some degree of flexibility in highly rigid PMMA chains. Molecular level interchain mixing was found to be more efficient at a PMMA concentration of 35 wt.%. Moreover, the strong interphase formed in the above mentionedsemi IPN contributed to the large slow component in the ESR spectra at higher temperature. The shape of the spectra along with the data obtained from the simulations of spectra was correlated to the morphology of the semi IPNs. The correlation time measurement detected the motional region associated with the glass transition of PI and PMMA, and these regions were found to follow the same pattern of shifts in a-relaxation of PI and PMMA observed in DMA analysis. Activation energies associated with the T-g regions were also calculated. T-50G was found to correlate with the T-g of PMMA, and the volume of polymer segments undergoing glass transitional motion was calculated to be 1.7 nm(3).C-13 T-1 rho measurements of PMMA carbons indicate that the molecular level interactions were strong in semi IPN irrespective of the immiscible nature of polymers. The motional characteristics of H atoms attached to carbon atoms in both polymers were analyzed using 2D WISE NMR. Main relaxations of both components shifted inward, and both SEM and TEM analysis showed the development of a nanometer sized morphology in the case of highly crosslinked semi IPN. (C) 2010 Elsevier Ltd. All rights reserved.

Inductively coupled plasma etching in fabrication of 2D InP-based photonic crystals

The authors developed an inductively coupled plasma etching process for the fabrication of hole-type photonic crystals in InP. The etching was performed at 70 degrees C using BCl3/Cl-2 chemistries. A high etch rate of 1.4 mu m/min was obtained for 200 nm diameter holes. The process also yields nearly cylindrical hole shape with a 10.8 aspect ratio and more than 85 degrees straightness of the smooth sidewall. Surface-emitting photonic crystal laser and edge emitting one were demonstrated in the experiments.

Optical interleaver based on directional coupler in a 2D photonic crystal slab with triangular lattice of air holes

A small-size optical interleaver based on directional coupler in a 2D photonic crystal slab with triangular lattice of air holes is designed and theoretically simulated using plane wave expansion and finite-difference time-domain method. The interleaver is formed by two parallel and identical photonic crystal slab waveguides which are separated by three rows of air holes. The coupling region is designed below the light line to avoid vertical radiation. The simulated results show that the coupling coefficient is increased and the final length of the interleaver is decreased by enlarging the radius of the middle row of air holes. The transmission properties are analyzed after the interleaver's structure is optimized, and around 100 GHz channel spacing can be got when the length of the interleaver is chosen as 40.5 mu m. (c) 2006 Elsevier B.V. All rights reserved.

Theoretical analysis about the influence of channel layer thickness on the 2D electron gas and its distribution in InP-based high-electron-mobility transistors

The principle of high-electron-mobility transistor (HEMT) and the property of two-dimensional electron gas (2DEG) have been analyzed theoretically. The concentration and distribution of 2DEG in various channel layers are calculated by numerical method. Variation of 2DEG concentration in different subband of the quantum well is discussed in detail. Calculated results show that sheet electron concentration of 2DEG in the channel is affected slightly by the thickness of the channel. But the proportion of electrons inhabited in different subbands can be affected by the thickness of the channel. When the size of channel lies between 20-25 nm, the number of electrons occupying the second subband reaches the maximum. This result can be used in parameter design of materials and devices.

Size quantization effects in InAs self-assembled islands on InP(001) at the onset of 2D-to-3D transition

Photoluminescence spectroscopy has been used to investigate self-assembled InAs islands in InAlAs grown on InP(0 0 1) by molecular beam epitaxy, in correlation with transmission electron microscopy. The nominal deposition of 3.6 monolayers of InAs at 470 degrees C achieves the onset stage of coherent island formation. In addition to one strong emission around 0.74 eV, the sample displaces several emission peaks at 0.87, 0.92. 0.98, and 1.04 eV. Fully developed islands that coexist with semi-finished disk islands account for the multipeak emission. These results provide strong evidence of size quantization effects in InAs islands. (C) 1999 Elsevier Science B.V. All rights reserved.

基于HMM-FNN模型的复杂动态手势识别

复杂动态手势识别是利用视频手势进行人机交互的关键问题.提出一种HMM-FNN模型结构.它整合了隐马尔可夫模型对时序数据的建模能力与模糊神经网络的模糊规则构建与推理能力,并将其应用到复杂动态手势的识别中.复杂动态手势具备两大特点:运动特征的可分解性与定义描述的模糊性.针对这两种特性,复杂手势被分解为手形变化、2D平面运动与Z轴方向运动3个子部分,分别利用HMM进行建模,HMM模型对观察子序列的似然概率被作为FNN的模糊隶属度,通过模糊规则推理,最终得到手势的分类类别.HMM-FNN方法将高维手势特征分解为低维子特征序列,降低了模型的复杂度.此外,它还可以充分利用人的经验辅助模型结构的创建与优化.实验表明,该方法是一种有效的复杂动态手势识别方法,并且优于传统的HMM模型方法.

DISORDER POINT FOR 2D ELECTRONS IN A HALF-FILLED LANDAU-LEVEL

Short-range correlations of two-dimensional electrons in a strong magnetic field are shown to be triangular in nature well below half-filling, but honeycomb well above half-filling. The half-filling point is thus proposed, and qualitatively confirmed by three-body correlation calculations, to be a new type of disorder point where short-range correlations change character. A wavefunction study also suggests that nodes become unbound at half-filling. Evidence for incompressibility but deformability of the half-filling state earlier suggested by Fano, Ortolani and Tosatti, is also presented and found to be in agreement with recent experiments.

An All-E-Beam Lithography Process for the Patterning of 2D Photonic Crystal Waveguide Devices

We present an all-e-beam lithography (EBL) process for the patterning of photonic crystal waveguides.The whole device structures are exposed in two steps. Holes constituting the photonic crystal lattice and defects are first exposed with a small exposure step size (less than 10nm). With the introduction of the additional proximity effect to compensate the original proximity effect, the shape, size, and position of the holes can be well controlled.The second step is the exposure of the access waveguides at a larger step size (about 30nm) to improve the scan speed of the EBL. The influence of write-field stitching error can be alleviated by replacing the original waveguides with tapered waveguides at the joint of adjacent write-fields. It is found experimentally that a higher exposure efficiency is achieved with a larger step size;however,a larger step size requires a higher dose.