Implementation of Deep Learning-based Object Recognition and Tracking for Intelligent Video Surveillance

As research on artificial intelligence technology is actively conducted in recent years, research on deep learning technology that recognizes and classifies images in real time on behalf of humans is required. Object recognition has difficulties in finding an object of interest from a video clip or image, and classifying several detected objects for each object. To solve this problem, research is needed to detect and track objects using a CNN-based deep learning method. Among the CNN-based multi objects detection techniques, the most well-known methods are R-CNN and Faster R-CNN. These methods are based on ROI-based detection techniques to perform verification work to reduce candidate groups through pre-work in the ROI of the object. However, since the process of classifying objects for each region of interest is performed, the detector speed decreases. Real-time processing is not possible due to this speed problem. In this paper, to overcome these issues, we have proposed multi object detection, classification, and tracking method using YOLO, a single step technique that performs a single CNN to determine the location and type of objects in an image. Experimental results depict that it can detect and classify objects robustly in various environments, and that real-time tracking is possible because the calculation speed is faster than the conventional method

LAD Asymptotics under Conditional Heteroskedasticity with Possibly Infinite Error Densities

Least absolute deviations (LAD) estimation of linear time-series models is considered under conditional heteroskedasticity and serial correlation. The limit theory of the LAD estimator is obtained without assuming the finite density condition for the errors that is required in standard LAD asymptotics. The results are particularly useful in application of LAD estimation to financial time series data.Asymptotic leptokurtosis, Convex function, Infinite density, Least absolute deviations, Median, Weak convergence

Infinite Density at the Median and the Typical Shape of Stock Return Distributions

Statistics are developed to test for the presence of an asymptotic discontinuity (or infinite density or peakedness) in a probability density at the median. The approach makes use of work by Knight (1998) on L_1 estimation asymptotics in conjunction with non-parametric kernel density estimation methods. The size and power of the tests are assessed, and conditions under which the tests have good performance are explored in simulations. The new methods are applied to stock returns of leading companies across major U.S. industry groups. The results confirm the presence of infinite density at the median as a new significant empirical evidence for stock return distributions.Asymptotic leptokurtosis, Infinite density at the median, Least absolute deviations, Kernel density estimation, Stock returns, Stylized facts

Infinite Density at the Median and the Typical Shape of Stock Return Distributions

Statistics are developed to test for the presence of an asymptotic discontinuity (or infinite density or peakedness) in a probability density at the median. The approach makes use of work by Knight (1998) on L 1 estimation asymptotics in conjunction with non-parametric kernel density estimation methods. The size and power of the tests are assessed, and conditions under which the tests have good performance are explored in simulations. The new methods are applied to stock returns of leading companies across major U.S. industry groups. The results confirm the presence of infinite density at the median as a new significant empirical evidence for stock return distributions

LAD Asymptotics under Conditional Heteroskedasticity with Possibly Infinite Error Densities

Least absolute deviations (LAD) estimation of linear time-series models is considered under conditional heteroskedasticity and serial correlation. The limit theory of the LAD estimator is obtained without assuming the finite density condition for the errors that is required in standard LAD asymptotics. The results are particularly useful in application of LAD estimation to financial time series data

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Structural health monitoring (SHM) is a technique to diagnose an accurate and reliable condition of civil infrastructure by collecting and analyzing responses from distributed sensors. In recent years, aging civil structures have been increasing and they require further developed SHM technology for development of sustainable society. Wireless smart sensor and network technology, which is one of the recently emerging SHM techniques, enables more effective and economic SHM system in comparison to the existing wired systems. Researchers continue on development of the capability and extension of wireless smart sensors, and implement performance validation in various in-laboratory and outdoor full-scale experiments. This paper presents a summary of recent (mostly after 2010) researches on smart sensors, focused on the newly developed hardware, software, and validation examples of the developed smart sensors.ope

Role of Staphylococcal Superantigen in Atopic Dermatitis: Influence on Keratinocytes

Staphylococcus aureus may perform an crucial function in atopic dermatitis (AD), via the secretion of superantigens, including staphylococcal enterotoxins (SE) A or B, and toxic shock syndrome toxin-1 (TSST-1). Dysregulated cytokine production by keratinocytes (KCs) upon exposure to staphylococcal superantigens (SsAgs) may be principally involved in the pathophysiology of AD. We hypothesized that lesional KCs from AD may react differently to SsAgs compared to nonlesional skin or normal skin from nonatopics. We conducted a comparison of HLA-DR or CD1a expression in lesional skin as opposed to that in nonlesional or normal skin by immunohistochemistry (IHC). We also compared, using ELISA, the levels of IL-1α, IL-1β, and TNF-α secreted by cultured KCs from lesional, nonlesional, and normal skin, after the addition of SEA, SEB and TSST-1. IHC revealed that both HLA-DR and CD1a expression increased significantly in the epidermis of lesional skin versus nonlesional or normal skin in quite a similar manner. IL-1α, IL-1β, and TNF-α secretion was also significantly elevated in the cultured KCs from lesional skin after the addition of SsAgs. Our results indicated that KCs from lesional skin appear to react differently to SsAgs and increased proinflammatory cytokine production in response to SsAgs may contribute to the pathogenesis of AD