Learning to Detect Violent Videos using Convolutional Long Short-Term Memory

Developing a technique for the automatic analysis of surveillance videos in order to identify the presence of violence is of broad interest. In this work, we propose a deep neural network for the purpose of recognizing violent videos. A convolutional neural network is used to extract frame level features from a video. The frame level features are then aggregated using a variant of the long short term memory that uses convolutional gates. The convolutional neural network along with the convolutional long short term memory is capable of capturing localized spatio-temporal features which enables the analysis of local motion taking place in the video. We also propose to use adjacent frame differences as the input to the model thereby forcing it to encode the changes occurring in the video. The performance of the proposed feature extraction pipeline is evaluated on three standard benchmark datasets in terms of recognition accuracy. Comparison of the results obtained with the state of the art techniques revealed the promising capability of the proposed method in recognizing violent videos.Comment: Accepted in International Conference on Advanced Video and Signal based Surveillance(AVSS 2017

Development of a Computer Vision-Based Three-Dimensional Reconstruction Method for Volume-Change Measurement of Unsaturated Soils during Triaxial Testing

Problems associated with unsaturated soils are ubiquitous in the U.S., where expansive and collapsible soils are some of the most widely distributed and costly geologic hazards. Solving these widespread geohazards requires a fundamental understanding of the constitutive behavior of unsaturated soils. In the past six decades, the suction-controlled triaxial test has been established as a standard approach to characterizing constitutive behavior for unsaturated soils. However, this type of test requires costly test equipment and time-consuming testing processes. To overcome these limitations, a photogrammetry-based method has been developed recently to measure the global and localized volume-changes of unsaturated soils during triaxial test. However, this method relies on software to detect coded targets, which often requires tedious manual correction of incorrectly coded target detection information. To address the limitation of the photogrammetry-based method, this study developed a photogrammetric computer vision-based approach for automatic target recognition and 3D reconstruction for volume-changes measurement of unsaturated soils in triaxial tests. Deep learning method was used to improve the accuracy and efficiency of coded target recognition. A photogrammetric computer vision method and ray tracing technique were then developed and validated to reconstruct the three-dimensional models of soil specimen

AFM imaging and plasmonic detection of organic thin-films deposited on nanoantenna arrays

In this study, atomic force microscopy (AFM) imaging has been used to reveal the preferential deposition of organic thin-films on patterned nanoantenna array surfaces - identifying the localised formation of both monolayer and multilayer films of octadecanethiol (ODT) molecules, depending on the concentration of the solutions used. Reliable identification of this selective deposition process has been demonstrated for the first time, to our knowledge. Organic thin-films, in particular films of ODT molecules, were deposited on plasmonic resonator surfaces through a chemi-sorption process - using different solution concentrations and immersion times. The nanoantennas based on gold asymmetric-split ring resonator (A-SRR) geometries were fabricated on zinc selenide (ZnSe) substrates using electron-beam lithography and the lift-off technique. Use of the plasmonic resonant-coupling technique has enabled the detection of ODT molecules deposited from a dilute, micromolar (1 M) solution concentration - with attomole sensitivity of deposited material per A-SRR – a value that is three orders of magnitude lower in concentration than previously reported. Additionally, on resonance, the amplitude of the molecular vibrational resonance peaks is typically an order of magnitude larger than that for the non-resonant coupling. Fourier-transform infrared (FTIR) spectroscopy shows molecule specific spectral responses – with magnitudes corresponding to the different film thicknesses deposited on the resonator surfaces. The experimental results are supported by numerical simulation

Phase Stability and Segregation in Alloy 22 Base Metal and Weldments

The current design of the waste disposal containers relies heavily on encasement in a multi-layered container, featuring a corrosion barrier of Alloy 22, a Ni-Cr-Mo-W based alloy with excellent corrosion resistance over a wide range of conditions. The fundamental concern from the perspective of the Yucca Mountain Project, however, is the inherent uncertainty in the (very) long-term stability of the base metal and welds. Should the properties of the selected materials change over the long service life of the waste packages, it is conceivable that the desired performance characteristics (such as corrosion reistance) will become compromised, leading to premature failure of the system. To address this, we will study the phase stability and solute segregation characteristics of Alloy 22 base metal and welds. A better understanding of the underlying microstructural evolution tendencies, and their connections with corrosion behavior will (in turn) produce a higher confidence in the extrapolated behavior of the container materials over time periods that are not feasibly tested in a laboratory. Additionally, the knowledge gained here may potentially lead to cost savings through development of safe and realistic design constraints and model assumptions throughout the entire disposal system

Recognizing Degraded Handwritten Characters

In this paper, Slavonic manuscripts from the 11th century written in Glagolitic script are investigated. State-of-the-art optical character recognition methods produce poor results for degraded handwritten document images. This is largely due to a lack of suitable results from basic pre-processing steps such as binarization and image segmentation. Therefore, a new, binarization-free approach will be presented that is independent of pre-processing deficiencies. It additionally incorporates local information in order to recognize also fragmented or faded characters. The proposed algorithm consists of two steps: character classification and character localization. Firstly scale invariant feature transform features are extracted and classified using support vector machines. On this basis interest points are clustered according to their spatial information. Then, characters are localized and eventually recognized by a weighted voting scheme of pre-classified local descriptors. Preliminary results show that the proposed system can handle highly degraded manuscript images with background noise, e.g. stains, tears, and faded characters

Efficient Scene Text Localization and Recognition with Local Character Refinement

An unconstrained end-to-end text localization and recognition method is presented. The method detects initial text hypothesis in a single pass by an efficient region-based method and subsequently refines the text hypothesis using a more robust local text model, which deviates from the common assumption of region-based methods that all characters are detected as connected components. Additionally, a novel feature based on character stroke area estimation is introduced. The feature is efficiently computed from a region distance map, it is invariant to scaling and rotations and allows to efficiently detect text regions regardless of what portion of text they capture. The method runs in real time and achieves state-of-the-art text localization and recognition results on the ICDAR 2013 Robust Reading dataset

Automatic detection of change in address blocks for reply forms processing

In this paper, an automatic method to detect the presence of on-line erasures/scribbles/corrections/over-writing in the address block of various types of subscription and utility payment forms is presented. The proposed approach employs bottom-up segmentation of the address block. Heuristic rules based on structural features are used to automate the detection process. The algorithm is applied on a large dataset of 5,780 real world document forms of 200 dots per inch resolution. The proposed algorithm performs well with an average processing time of 108 milliseconds per document with a detection accuracy of 98.96%