Lost in Time: Temporal Analytics for Long-Term Video Surveillance

Video surveillance is a well researched area of study with substantial work done in the aspects of object detection, tracking and behavior analysis. With the abundance of video data captured over a long period of time, we can understand patterns in human behavior and scene dynamics through data-driven temporal analytics. In this work, we propose two schemes to perform descriptive and predictive analytics on long-term video surveillance data. We generate heatmap and footmap visualizations to describe spatially pooled trajectory patterns with respect to time and location. We also present two approaches for anomaly prediction at the day-level granularity: a trajectory-based statistical approach, and a time-series based approach. Experimentation with one year data from a single camera demonstrates the ability to uncover interesting insights about the scene and to predict anomalies reasonably well.Comment: To Appear in Springer LNE

Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition

Facial micro-expression (ME) recognition has posed a huge challenge to researchers for its subtlety in motion and limited databases. Recently, handcrafted techniques have achieved superior performance in micro-expression recognition but at the cost of domain specificity and cumbersome parametric tunings. In this paper, we propose an Enriched Long-term Recurrent Convolutional Network (ELRCN) that first encodes each micro-expression frame into a feature vector through CNN module(s), then predicts the micro-expression by passing the feature vector through a Long Short-term Memory (LSTM) module. The framework contains two different network variants: (1) Channel-wise stacking of input data for spatial enrichment, (2) Feature-wise stacking of features for temporal enrichment. We demonstrate that the proposed approach is able to achieve reasonably good performance, without data augmentation. In addition, we also present ablation studies conducted on the framework and visualizations of what CNN "sees" when predicting the micro-expression classes.Comment: Published in Micro-Expression Grand Challenge 2018, Workshop of 13th IEEE Facial & Gesture 201

Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition

In the recent year, state-of-the-art for facial micro-expression recognition have been significantly advanced by deep neural networks. The robustness of deep learning has yielded promising performance beyond that of traditional handcrafted approaches. Most works in literature emphasized on increasing the depth of networks and employing highly complex objective functions to learn more features. In this paper, we design a Shallow Triple Stream Three-dimensional CNN (STSTNet) that is computationally light whilst capable of extracting discriminative high level features and details of micro-expressions. The network learns from three optical flow features (i.e., optical strain, horizontal and vertical optical flow fields) computed based on the onset and apex frames of each video. Our experimental results demonstrate the effectiveness of the proposed STSTNet, which obtained an unweighted average recall rate of 0.7605 and unweighted F1-score of 0.7353 on the composite database consisting of 442 samples from the SMIC, CASME II and SAMM databases.Comment: 5 pages, 1 figure, Accepted and published in IEEE FG 201

Tunable-Focus Liquid Lens through Charge Injection

Liquid lenses are the simplest and cheapest optical lenses, and various studies have been conducted to develop tunable-focus liquid lenses. In this study, a simple and easily implemented method for achieving tunable-focus liquid lenses was proposed and experimentally validated. In this method, charges induced by a corona discharge in the air were injected into dielectric liquid, resulting in “electropressure” at the interface between the air and the liquid. Through a 3D-printed U-tube structure, a tunable-focus liquid lens was fabricated and tested. Depending on the voltage, the focus of the liquid lens can be adjusted in large ranges (−∞ to −9 mm and 13.11 mm to ∞). The results will inspire various new liquid-lens applications

Electrical stress on the medium voltage medium frequency transformer

This paper proposes an equivalent circuit model to obtain the transient electrical stress quantitatively in medium voltage medium frequency transformers in modern power electronics. To verify this model, transient simulation is performed on a 1.5 kV/1 kHz transformer, revealing voltage overshoot quantitatively between turns and layers of the transformer’s HV winding. Effects of rise time of the input pulse voltage, stray capacitance of the winding insulation, and their interactions on the voltage overshot magnitude are presented. With these results, we propose limiting the voltage overshoot and, thereafter, enhancing medium voltage medium frequency transformer’s insulation capability, which throws light on the transformer’s insulation design. Additionally, guidance on the future studies on aging and endurance lifetime of the medium voltage medium frequency transformer’s insulation could be given

The mechanism of the polarization dependence of the optical transmission in subwavelength metal hole arrays

We investigate the mechanism of extraordinary optical transmission in subwave-length metal hole arrays. Experimental results for the arrays consisting of square or rectangle holes are well explained about the dependence of transmission strength on the polarization direction of the incident light. This polarization dependence occurs in each single-hole. For a hole array, there is in addition an interplay between the adjacent holes which is caused by the transverse magnetic field of surface plasmon polariton on the metal film surfaces. Based on the detailed study of a single-hole and two-hole structures, a simple method to calculate the total tranmissivity of hole arrays is proposed.Comment: 34 pages, 7 figure