Multi-View Region Adaptive Multi-temporal DMM and RGB Action Recognition

Human action recognition remains an important yet challenging task. This work proposes a novel action recognition system. It uses a novel Multiple View Region Adaptive Multi-resolution in time Depth Motion Map (MV-RAMDMM) formulation combined with appearance information. Multiple stream 3D Convolutional Neural Networks (CNNs) are trained on the different views and time resolutions of the region adaptive Depth Motion Maps. Multiple views are synthesised to enhance the view invariance. The region adaptive weights, based on localised motion, accentuate and differentiate parts of actions possessing faster motion. Dedicated 3D CNN streams for multi-time resolution appearance information (RGB) are also included. These help to identify and differentiate between small object interactions. A pre-trained 3D-CNN is used here with fine-tuning for each stream along with multiple class Support Vector Machines (SVM)s. Average score fusion is used on the output. The developed approach is capable of recognising both human action and human-object interaction. Three public domain datasets including: MSR 3D Action,Northwestern UCLA multi-view actions and MSR 3D daily activity are used to evaluate the proposed solution. The experimental results demonstrate the robustness of this approach compared with state-of-the-art algorithms.Comment: 14 pages, 6 figures, 13 tables. Submitte

Vision based dynamic thermal comfort control using fuzzy logic and deep learning

A wide range of techniques exist to help control the thermal comfort of an occupant in indoor environments. A novel technique is presented here to adaptively estimate the occupant’s metabolic rate. This is performed by utilising occupant’s actions using computer vision system to identify the activity of an occupant. Recognized actions are then translated into metabolic rates. The widely used Predicted Mean Vote (PMV) thermal comfort index is computed using the adaptivey estimated metabolic rate value. The PMV is then used as an input to a fuzzy control system. The performance of the proposed system is evaluated using simulations of various activities. The integration of PMV thermal comfort index and action recognition system gives the opportunity to adaptively control occupant’s thermal comfort without the need to attach a sensor on an occupant all the time. The obtained results are compared with the results for the case of using one or two fixed metabolic rates. The included results appear to show improved performance, even in the presence of errors in the action recognition system

Deep learning of fuzzy weighted multi-resolution depth motion maps with spatial feature fusion for action recognition

Human action recognition (HAR) is an important yet challenging task. This paper presents a novel method. First, fuzzy weight functions are used in computations of depth motion maps (DMMs). Multiple length motion information is also used. These features are referred to as fuzzy weighted multi-resolution DMMs (FWMDMMs). This formulation allows for various aspects of individual actions to be emphasized. It also helps to characterise the importance of the temporal dimension. This is important to help overcome, e.g., variations in time over which a single type of action might be performed. A deep convolutional neural network (CNN) motion model is created and trained to extract discriminative and compact features. Transfer learning is also used to extract spatial information from RGB and depth data using the AlexNet network. Different late fusion techniques are then investigated to fuse the deep motion model with the spatial network. The result is a spatial temporal HAR model. The developed approach is capable of recognising both human action and human–object interaction. Three public domain datasets are used to evaluate the proposed solution. The experimental results demonstrate the robustness of this approach compared with state-of-the art algorithms

Narghile (water pipe) smoking among university students in Jordan: prevalence, pattern and beliefs

<p>Abstract</p> <p>Background and objectives</p> <p>Narghile is becoming the favorite form of tobacco use by youth globally. This problem has received more attention in recent years. The aim of this study was to investigate the prevalence and pattern of narghile use among students in three public Jordanian universities; to assess their beliefs about narghile's adverse health consequences; and to evaluate their awareness of oral health and oral hygiene.</p> <p>Methods</p> <p>The study was a cross-sectional survey of university students. A self-administered, anonymous questionnaire was distributed randomly to university students in three public Jordanian universities during December, 2008. The questionnaire was designed to ask specific questions that are related to smoking in general, and to narghile smoking in specific. There were also questions about oral health awareness and oral hygiene practices.</p> <p>Results</p> <p>36.8% of the surveyed sample indicated they were smokers comprising 61.9% of the male students and 10.7% of the female students in the study sample. Cigarettes and narghile were the preferred smoking methods among male students (42%). On the other hand, female students preferred narghile only (53%). Parental smoking status but not their educational level was associated with the students smoking status. Smokers had also significantly poor dental attendance and poor oral hygiene habits.</p> <p>Conclusion</p> <p>This study confirmed the spreading narghile epidemic among young people in Jordan like the neighboring countries of the Eastern Mediterranean region. Alarming signs were the poor oral health awareness among students particularly smokers.</p