Unmasking the abnormal events in video

We propose a novel framework for abnormal event detection in video that requires no training sequences. Our framework is based on unmasking, a technique previously used for authorship verification in text documents, which we adapt to our task. We iteratively train a binary classifier to distinguish between two consecutive video sequences while removing at each step the most discriminant features. Higher training accuracy rates of the intermediately obtained classifiers represent abnormal events. To the best of our knowledge, this is the first work to apply unmasking for a computer vision task. We compare our method with several state-of-the-art supervised and unsupervised methods on four benchmark data sets. The empirical results indicate that our abnormal event detection framework can achieve state-of-the-art results, while running in real-time at 20 frames per second.Comment: Accepted at the 2017 International Conference on Computer Vision (ICCV 2017

Sensitive methods for estimating the anchoring strength of nematic liquid crystals on Langmuir-Blodgett monolayers of fatty acids

The anchoring of the nematic liquid crystal N-(p-methoxybenzylidene)-p-butylaniline (MBBA) on Langmuir-Blodgett monolayers of fatty acids (COOHC$_{n}$H$_{2n+1}$) was studied as a function of the length of the fatty acid alkyl chains, $n$ ($n = 15, 17, 19, 21$). The monolayers were deposited onto ITO-coated glass plates which were used to assemble sandwich cells of various thickness that were filled with MBBA in the nematic phase. The mechanism of relaxation from the flow-induced quasi-planar to the surface-induced homeotropic alignment was studied for the four decreases linearly with increasing the length of the alkyl chains $n$ which suggests that the Langmuir-Blodgett film plays a role in the phenomenon. This fact was confirmed by a sensitive estimation of the anchoring strength of MBBA on the fatty acid monolayers after anchoring breaking which takes place at the transition between two electric-field--induced turbulent states, denoted as DSM1 and DSM2. It was found that the threshold electric field for the anchoring breaking, which can be considered as a measure of the anchoring strength, also decreases linearly as $n$ increases. Both methods thus possess a high sensitivity in resolving small differences in anchoring strength. In cells coated with mixed Langmuir-Blodgett monolayers of two fatty acids ($n=15$ and $n=17$) a maximum of the relaxation speed was observed when the two acids were present in equal amount. This observation homeotropic cells by changing the ratio between the components of the surfactant film.Comment: LaTeX article, 20 pages, 15 figures, 17 EPS files. 1 figure added, references moved. Submitted to Phys. Rev.

Significance of small voltage in impedance spectroscopy measurements on electrolytic cells

We investigate, theoretically, for what amplitude of the applied voltage to an electrolytic cell the concept of impedance is meaningful. The analysis is performed by means of a continuum model, by assuming the electrodes perfectly blocking. We show that, in the low-frequency range, the electrolytic cell behaves as a linear system only if the amplitude of the measurement voltage is small with respect to the thermal voltage V(T)=k(B)T/q, where k(B)T is the thermal energy, and q is the modulus of the electrical charge of the ions, assumed identical except for the sign of the charge. On the contrary, for large frequency, we prove that the amplitude of the applied signal has to be small with respect to a critical voltage that is frequency dependent. The same kind of analysis is presented for the case in which the diffusion coefficients of the positive ions is different from that for negative ions, and for the case where surface adsorption takes place

Detecting abnormal events in video using Narrowed Normality Clusters

We formulate the abnormal event detection problem as an outlier detection task and we propose a two-stage algorithm based on k-means clustering and one-class Support Vector Machines (SVM) to eliminate outliers. In the feature extraction stage, we propose to augment spatio-temporal cubes with deep appearance features extracted from the last convolutional layer of a pre-trained neural network. After extracting motion and appearance features from the training video containing only normal events, we apply k-means clustering to find clusters representing different types of normal motion and appearance features. In the first stage, we consider that clusters with fewer samples (with respect to a given threshold) contain mostly outliers, and we eliminate these clusters altogether. In the second stage, we shrink the borders of the remaining clusters by training a one-class SVM model on each cluster. To detected abnormal events in the test video, we analyze each test sample and consider its maximum normality score provided by the trained one-class SVM models, based on the intuition that a test sample can belong to only one cluster of normality. If the test sample does not fit well in any narrowed normality cluster, then it is labeled as abnormal. We compare our method with several state-of-the-art methods on three benchmark data sets. The empirical results indicate that our abnormal event detection framework can achieve better results in most cases, while processing the test video in real-time at 24 frames per second on a single CPU.Comment: Accepted at WACV 2019. arXiv admin note: text overlap with arXiv:1705.0818

A Model for Bias Potential Effects on the Effective Langmuir Adsorption–Desorption Processes

We discuss the foundations of a model based on an extension of the Langmuir approximation for the adsorption–desorption phenomena, in which the phenomenological coefficients depend on the bias potential, in addition to their dependence on the adsorption energy. The theoretical analysis focuses on the effect of these effective coefficients on the electrical response of an electrolytic cell to an external electric field, as predicted by the Poisson–Nernst–Planck model. Kinetic balance equations govern the current densities on the electrodes when the adsorption phenomenon occurs in the presence of an electric bias. The influence of the phenomenological parameters entering the model, as well as of the symmetry of the cell on the cyclic voltammetry, is investigated

A simple model of ac hopping surface conductivity in ionic liquids

The boundary conditions proposed to discuss the charge exchange taking place in an ionic liquid in contact with non-blocking electrodes are reconsidered in a dynamic situation. Assuming that the variation of the bulk ionic current density depends linearly on the surface value of the ionic current density, the frequency dependence of the phenomenological parameter is determined. The analysis has been performed in the framework where the relaxation times are smaller than a maximum relaxation time τM, and that the response function is independent on the value of the relaxation time. Using simple physical considerations, an expression for the surface conductivity describing the ionic charge exchange at the electrode is obtained. According to our calculations, its frequency dependence is similar to that predicted for the electric conductivity in disordered materials when the mechanism is of the hopping type. From measurements of impedance spectroscopy, by the best fit of the experimental data, the temperature dependence of the hopping time, of the dc surface conductivity, and of the diffusion coefficient are derived. They are in good agreement with the theoretical predictions obtained with the random distribution of surface energy barrier. Keywords: Ionic liquids, Non-blocking electrodes, Electrical impedance spectroscopy, AC hopping surface conductivit