93,964 research outputs found

    Olympic Games Event Recognition via Adaptive Convolutional Neural Networks

    Get PDF
    Automatic event recognition based on human action is both interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data which is being captured momentarily, the need of fast and precise access to the right information has become challenging task with considerable importance for multiple practical applications, e.g., image and video search, sport data analysis, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and video captioning. This research, part of my master’s thesis, develops an adaptive content-aware convolution neural network with the capability of analyzing, recognizing and interpreting the sport event in the Olympic games based on human action. 20 of the 33 sports scheduled for inclusion in the Olympic Games Tokyo 2020 will be included in the collected data set to evaluate the proposed method. This method combines convolutional neural network (CNN) and transfer learning (fine-tuning method) to potentially achieve best performance with high accuracy and precision of the event recognition.https://ecommons.udayton.edu/stander_posters/2929/thumbnail.jp

    Abnormal Infant Movements Classification With Deep Learning on Pose-Based Features

    Get PDF
    The pursuit of early diagnosis of cerebral palsy has been an active research area with some very promising results using tools such as the General Movements Assessment (GMA). In our previous work, we explored the feasibility of extracting pose-based features from video sequences to automatically classify infant body movement into two categories, normal and abnormal. The classification was based upon the GMA, which was carried out on the video data by an independent expert reviewer. In this paper we extend our previous work by extracting the normalised pose-based feature sets, Histograms of Joint Orientation 2D (HOJO2D) and Histograms of Joint Displacement 2D (HOJD2D), for use in new deep learning architectures. We explore the viability of using these pose-based feature sets for automated classification within a deep learning framework by carrying out extensive experiments on five new deep learning architectures. Experimental results show that the proposed fully connected neural network FCNet performed robustly across different feature sets. Furthermore, the proposed convolutional neural network architectures demonstrated excellent performance in handling features in higher dimensionality. We make the code, extracted features and associated GMA labels publicly available

    CaloriNet: From silhouettes to calorie estimation in private environments

    Get PDF
    We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes. Our fused convolutional neural network architecture is trainable end-to-end, to estimate calorie expenditure, using temporal foreground silhouettes alongside accelerometer data. The network is trained and cross-validated on a publicly available dataset, SPHERE_RGBD + Inertial_calorie. Results show state-of-the-art minimum error on the estimation of energy expenditure (calories per minute), outperforming alternative, standard and single-modal techniques.Comment: 11 pages, 7 figure

    Detecting Irregular Patterns in IoT Streaming Data for Fall Detection

    Full text link
    Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to enable predictive analytics for automated notification and decision support. In this work, we address the problem of learning an irregular human activity pattern, fall, from streaming IoT data from wearable sensors. We present a deep neural network model for detecting fall based on accelerometer data giving 98.75 percent accuracy using an online physical activity monitoring dataset called "MobiAct", which was published by Vavoulas et al. The initial model was developed using IBM Watson studio and then later transferred and deployed on IBM Cloud with the streaming analytics service supported by IBM Streams for monitoring real-time IoT data. We also present the systems architecture of the real-time fall detection framework that we intend to use with mbientlabs wearable health monitoring sensors for real time patient monitoring at retirement homes or rehabilitation clinics.Comment: 7 page
    corecore