Sparse Representation based Anomaly Detection using HOMV in H.264 Compressed Videos

In this paper, we have proposed an anomaly detection algorithm based on Histogram of Oriented Motion Vectors (HOMV) 1] in sparse representation framework. Usual behavior is learned at each location by sparsely representing the HOMVs over learnt normal feature bases obtained using an online dictionary learning algorithm. In the end, anomaly is detected based on the likelihood of the occurrence of sparse coefficients at that location. The proposed approach is found to be robust compared to existing methods as demonstrated in the experiments on UCSD Ped1 and UCSD Ped2 datasets.

Anomaly detection in compressed H.264/AVC video

Real time anomaly detection is the need of the hour for any security applications. In this article, we have proposed a real time anomaly detection for H.264 compressed video streams utilizing pre-encoded motion vectors (MVs). The proposed work is principally motivated by the observation that MVs have distinct characteristics during anomaly than usual. Our observation shows that H.264 MV magnitude and orientation contain relevant information which can be used to model the usual behavior (UB) effectively. This is subsequently extended to detect abnormality/anomaly based on the probability of occurrence of a behavior. The performance of the proposed algorithm was evaluated and bench-marked on UMN and Ped anomaly detection video datasets, with a detection rate of 70 frames per sec resulting in 90x and 250x speedup, along with on-par detection accuracy compared to the state-of-the-art algorithms.

A survey on compressed domain video analysis techniques

Image and video analysis requires rich features that can characterize various aspects of visual information. These rich features are typically extracted from the pixel values of the images and videos, which require huge amount of computation and seldom useful for real-time analysis. On the contrary, the compressed domain analysis offers relevant information pertaining to the visual content in the form of transform coefficients, motion vectors, quantization steps, coded block patterns with minimal computational burden. The quantum of work done in compressed domain is relatively much less compared to pixel domain. This paper aims to survey various video analysis efforts published during the last decade across the spectrum of video compression standards. In this survey, we have included only the analysis part, excluding the processing aspect of compressed domain. This analysis spans through various computer vision applications such as moving object segmentation, human action recognition, indexing, retrieval, face detection, video classification and object tracking in compressed videos.

CROWD FLOW SEGMENTATION IN COMPRESSED DOMAIN USING CRF

Crowd flow segmentation is an important step in many video surveillance tasks. In this work, we propose an algorithm for segmenting flows in H.264 compressed videos in a completely unsupervised manner. Our algorithm works on motion vectors which can be obtained by partially decoding the compressed video without extracting any additional features. Our approach is based on modelling the motion vector field as a Conditional Random Field (CRF) and obtaining oriented motion segments by finding the optimal labelling which minimises the global energy of CRF. These oriented motion segments are recursively merged based on gradient across their boundaries to obtain the final flow segments. This work in compressed domain can be easily extended to pixel domain by substituting motion vectors with motion based features like optical flow. The proposed algorithm is experimentally evaluated on a standard crowd flow dataset and its superior performance in both accuracy and computational time are demonstrated through quantitative results.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

Liquefaction And Pore Water Pressure Generation In Sand - A Cyclic Strain Approach

This paper presents the results of laboratory investigation carried out on Ahmedabad sand on the liquefaction and pore water pressure generation during strain controled cyclic loading. Laboratory experiments were carried out on representative natural sand samples (base sand) collected from earthquake-affected area of Ahmedabad City of Gujarat State in India. A series of strain controled cyclic triaxial tests were carried out on isotropically compressed samples to study the influence of different parameters such as shear strain amplitude, initial effective confining pressure, relative density and percentage of non-plastic fines on the behavior of liquefaction and pore water pressure generation. It has been observed from the laboratory investigation that the potential for liquefaction of the sandy soils depends on the shear strain amplitude, initial relative density, initial effective confining pressure and non-plastic fines. In addition, an empirical relationship between pore pressure ratio and cycle ratio independent of the number of cycles of loading, relative density, confining pressure, amplitude of shear strain and non-plastic fines has been proposed.

Flow of metal in a wedge-shaped cavity

The wedge shape is a fairly common cross-section found in many non-axisymmetric components used in machines, aircraft, ships and automobiles. If such components are forged between two mutually inclined dies the metal displaced by the dies flows into the converging as well as into the diverging channels created by the inclined dies. The extent of each type of flow (convergent/divergent) depends on the die—material interface friction and the included die angle. Given the initial cross-section, the length as well as the exact geometry of the forged cross-section are therefore uniquely determined by these parameters. In this paper a simple stress analysis is used to predict changes in the geometry of a wedge undergoing compression between inclined platens. The flow in directions normal to the cross-section is assumed to be negligible. Experiments carried out using wedge-shaped lead billets show that, knowing the interface friction and as long as the deformation is not too large, the dimensional changes in the wedge can be predicted with reasonable accuracy. The predicted flow behaviour of metal for a wide range of die angles and interface friction is presented: these characteristics can be used by the die designer to choose the die lubricant (only) if the die angle is specified and to choose both of these parameters if there is no restriction on the exact die angle. The present work shows that the length of a wedge undergoing compression is highly sensitive to die—material interface friction. Thus in a situation where the top and bottom dies are inclined to each other, a wedge made of the material to be forged could be put between the dies and then compressed, whereupon the length of the compressed wedge — given the degree of compression — affords an estimate of the die—material interface friction.

Combustion studies on ammonium perchlorate/trimethylammonium perchlorate mixtures

A study of the burning rates of compressed mixtures of ammonium perchlorate (AP) and trimethylammonium perchlorate (TMAP) has been carried out at ambient pressure. The overall increase in the linear burning rate, showing a maximum at a composition having 80% TMAP, has been discussed in terms of factors such as stoichiometry, presence of faster burning component, and eutectic melt formation. The thermal decomposition studies of the mixtures, using isothermal thermogravimetry and differential thermal analysis techniques, indicate the possibility of eutectic melt formation.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

Wavelet transform based error concealment approach for image denoising

Denoising of images in compressed wavelet domain has potential application in transmission technology such as mobile communication. In this paper, we present a new image denoising scheme based on restoration of bit-planes of wavelet coefficients in compressed domain. It exploits the fundamental property of wavelet transform - its ability to analyze the image at different resolution levels and the edge information associated with each band. The proposed scheme relies on the fact that noise commonly manifests itself as a fine-grained structure in image and wavelet transform allows the restoration strategy to adapt itself according to directional features of edges. The proposed approach shows promising results when compared with conventional unrestored scheme, in context of error reduction and has capability to adapt to situations where noise level in the image varies. The applicability of the proposed approach has implications in restoration of images due to noisy channels. This scheme, in addition, to being very flexible, tries to retain all the features, including edges of the image. The proposed scheme is computationally efficient.

Wavelet transform based error concealment approach for image denoising

Denoising of images in compressed wavelet domain has potential application in transmission technology such as mobile communication. In this paper, we present a new image denoising scheme based on restoration of bit-planes of wavelet coefficients in compressed domain. It exploits the fundamental property of wavelet transform - its ability to analyze the image at different resolution levels and the edge information associated with each band. The proposed scheme relies on the fact that noise commonly manifests itself as a fine-grained structure in image and wavelet transform allows the restoration strategy to adapt itself according to directional features of edges. The proposed approach shows promising results when compared with conventional unrestored scheme, in context of error reduction and has capability to adapt to situations where noise level in the image varies. The applicability of the proposed approach has implications in restoration of images due to noisy channels. This scheme, in addition, to being very flexible, tries to retain all the features, including edges of the image. The proposed scheme is computationally efficient.

On characterization of deformation microstructure in Boron modified Ti-6Al-4V alloy

Addition of boron in small quantities to various titanium alloys have shown significant improvement in mechanical behavior of materials. In the present study, electron back-scatter diffraction (EBSD) techniques have been applied to investigate the deformation microstructure evolution in boron modified two-phase titanium alloy Ti-6Al-4V. The alloy was hot compressed at 750 degrees C up to 50% height reduction at two different strain rates (10(-3) s(-1) and 1 s(-1)). The EBSD analyses indicated significant differences in deformed microstructure of the base alloy and the alloy containing boron. A strong subgrain formation tendency was observed along with inhomogeneous distribution of dislocations inside large a colonies of Ti64. In contrast, a colonies were relatively strain free for Ti64 + B, with more uniform dislocation density distribution. The observed difference is attributed to microstructural modifications viz, grain size refinement and presence of TiB particles at grain boundary produced due to boron addition. (C) 2010 Elsevier B.V. All rights reserved.

Microstructural features of flow instability in α-titanium cylinders under high strain rate compression at 25°C to 400°C

Cylindrical specimens of commercial pure titanium have been compressed at strain rates in the range of 0.1 to 100 s-1 and temperatures in the range of 25-degrees-C to 400-degrees-C. At strain rates of 10 and 100 s-1, the specimens exhibited adiabatic shear bands. At lower strain rates, the material deformed in an inhomogeneous fashion. These material-related instabilities are examined in the light of the ''phenomenological model'' and the ''dynamic materials mode.'' It is found that the regime of adiabatic shear band formation is predicted by the phenomenological model, while the dynamic materials model is able to predict the inhomogeneous deformation zone. The criterion based on power partitioning is competent to predict the variations within the inhomogeneous deformation zone.

X-ray diffraction line profile analysis of deformation microstructure in boron modified Ti-6Al-4V alloy

X-ray diffraction line profile analysis (XRDLPA) techniques have been applied to investigate the deformed microstructure of a recently developed boron modified two-phase titanium alloy Ti-6Al-4V. The alloy was hot compressed at 750 degrees C up to 50% height reduction at two different strain rates (10(-3) S-1 and 1 S-1). Microstructural parameters like average domain size, average microstrain within the domain and dislocation density of the two phases were determined using X-ray diffraction line profile analysis. The results indicate an increase in the microstrain and dislocation density for the alpha-phase and decrease for the beta-phase in the case of boron modified alloys as compared to the normal material. Microstructural modifications viz, the grain refinement and the presence of hard, brittle TiB particles in the case of boron modified alloy are held responsible for the observed difference in the dislocation density. (C) 2010 Elsevier Inc. All rights reserved.