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Deep Learning Solutions For Continuous Action Recognition Using Fusion Of Inertial And Video Sensing And For Far Field Video Surveillance
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Book Synopsis Deep Learning Solutions for Continuous Action Recognition Using Fusion of Inertial and Video Sensing and for Far Field Video Surveillance by : Haoran Wei
Download or read book Deep Learning Solutions for Continuous Action Recognition Using Fusion of Inertial and Video Sensing and for Far Field Video Surveillance written by Haoran Wei and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation addresses deep learning solutions for two applications. The first application involves performing continuous human action recognition by simultaneous utilization of inertial and video sensing. The objective in this application is to achieve a more robust continuous action recognition compared to using a single sensing modality by simultaneously utilizing a video camera and a wearable inertial sensor. A deep learning solution is developed that differs from the action recognition approaches reported in the literature in two ways: (i) The detection and recognition of actions are carried out for continuous action streams and not on segmented actions, which is the assumption normally made in existing action recognition approaches. (ii) It provides the first attempt at using video and inertial sensing together or simultaneously in order to achieve continuous action recognition. As part of this effort, a Continuous Multimodal Human Action Dataset (named C-MHAD) is collected and made publicly available. The second application involves detecting persons and the load they carry in far field video surveillance data. The objective in this application is to detect persons and to classify the load carried by them from video data captured from distances several miles away via high-power lens video cameras. A deep learning solution is developed to cope with the following two major challenges: (i) Far field video data suffer from various noises caused by wind, heat haze, and the camera being out of focus thus generating blurriness of persons appearing in video images. (ii) The available dataset is small and lack no frame-level labels. The results obtained indicate the effectiveness of the developed deep learning solutions.
Book Synopsis Action Recognition in Continuous Data Streams Using Fusion of Depth and Inertial Sensing by : Neha Dawar
Download or read book Action Recognition in Continuous Data Streams Using Fusion of Depth and Inertial Sensing written by Neha Dawar and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Human action or gesture recognition has been extensively studied in the literature spanning a wide variety of human-computer interaction applications including gaming, surveillance, healthcare monitoring, and assistive living. Sensors used for action or gesture recognition are primarily either vision-based sensors or inertial sensors. Compared to the great majority of previous works where a single modality sensor is used for action or gesture recognition, the simultaneous utilization of a depth camera and a wearable inertial sensor is considered in this dissertation. Furthermore, compared to the great majority of previous works in which actions are assumed to be segmented actions, this dissertation addresses a more realistic and practical scenario in which actions of interest occur continuously and randomly amongst arbitrary actions of non-interest. In this dissertation, computationally efficient solutions are presented to recognize actions of interest from continuous data streams captured simultaneously by a depth camera and a wearable inertial sensor. These solutions comprise three main steps of segmentation, detection, and classification. In the segmentation step, all motion segments are extracted from continuous action streams. In the detection step, the segmented actions are separated into actions of interest and actions of non- interest. In the classification step, the detected actions of interest are classified. The features considered include skeleton joint positions, depth motion maps, and statistical attributes of acceleration and angular velocity inertial signals. The classifiers considered include maximum entropy Markov model, support vector data description, collaborative representation classifier, convolutional neural network, and long short-term memory network. These solutions are applied to the two applications of smart TV hand gestures and transition movements for home healthcare monitoring. The results obtained indicate the effectiveness of the developed solutions in detecting and recognizing actions of interest in continuous data streams. It is shown that higher recognition rates are achieved when fusing the decisions from the two sensing modalities as compared to when each sensing modality is used individually. The results also indicate that the deep learning-based solution provides the best outcome among the solutions developed.
Book Synopsis Human Action Recognition with Depth Cameras by : Jiang Wang
Download or read book Human Action Recognition with Depth Cameras written by Jiang Wang and published by Springer Science & Business Media. This book was released on 2014-01-25 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: Action recognition technology has many real-world applications in human-computer interaction, surveillance, video retrieval, retirement home monitoring, and robotics. The commoditization of depth sensors has also opened up further applications that were not feasible before. This text focuses on feature representation and machine learning algorithms for action recognition from depth sensors. After presenting a comprehensive overview of the state of the art, the authors then provide in-depth descriptions of their recently developed feature representations and machine learning techniques, including lower-level depth and skeleton features, higher-level representations to model the temporal structure and human-object interactions, and feature selection techniques for occlusion handling. This work enables the reader to quickly familiarize themselves with the latest research, and to gain a deeper understanding of recently developed techniques. It will be of great use for both researchers and practitioners.
Book Synopsis Vision-Based Human Activity Recognition by : Zhongxu Hu
Download or read book Vision-Based Human Activity Recognition written by Zhongxu Hu and published by Springer Nature. This book was released on 2022-04-22 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a systematic, comprehensive, and timely review on V-HAR, and it covers the related tasks, cutting-edge technologies, and applications of V-HAR, especially the deep learning-based approaches. The field of Human Activity Recognition (HAR) has become one of the trendiest research topics due to the availability of various sensors, live streaming of data and the advancement in computer vision, machine learning, etc. HAR can be extensively used in many scenarios, for example, medical diagnosis, video surveillance, public governance, also in human–machine interaction applications. In HAR, various human activities such as walking, running, sitting, sleeping, standing, showering, cooking, driving, abnormal activities, etc., are recognized. The data can be collected from wearable sensors or accelerometer or through video frames or images; among all the sensors, vision-based sensors are now the most widely used sensors due to their low-cost, high-quality, and unintrusive characteristics. Therefore, vision-based human activity recognition (V-HAR) is the most important and commonly used category among all HAR technologies. The addressed topics include hand gestures, head pose, body activity, eye gaze, attention modeling, etc. The latest advancements and the commonly used benchmark are given. Furthermore, this book also discusses the future directions and recommendations for the new researchers.
Book Synopsis Continuous Models for Cameras and Inertial Sensors by : Hannes Ovrén
Download or read book Continuous Models for Cameras and Inertial Sensors written by Hannes Ovrén and published by Linköping University Electronic Press. This book was released on 2018-07-25 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using images to reconstruct the world in three dimensions is a classical computer vision task. Some examples of applications where this is useful are autonomous mapping and navigation, urban planning, and special effects in movies. One common approach to 3D reconstruction is ”structure from motion” where a scene is imaged multiple times from different positions, e.g. by moving the camera. However, in a twist of irony, many structure from motion methods work best when the camera is stationary while the image is captured. This is because the motion of the camera can cause distortions in the image that lead to worse image measurements, and thus a worse reconstruction. One such distortion common to all cameras is motion blur, while another is connected to the use of an electronic rolling shutter. Instead of capturing all pixels of the image at once, a camera with a rolling shutter captures the image row by row. If the camera is moving while the image is captured the rolling shutter causes non-rigid distortions in the image that, unless handled, can severely impact the reconstruction quality. This thesis studies methods to robustly perform 3D reconstruction in the case of a moving camera. To do so, the proposed methods make use of an inertial measurement unit (IMU). The IMU measures the angular velocities and linear accelerations of the camera, and these can be used to estimate the trajectory of the camera over time. Knowledge of the camera motion can then be used to correct for the distortions caused by the rolling shutter. Another benefit of an IMU is that it can provide measurements also in situations when a camera can not, e.g. because of excessive motion blur, or absence of scene structure. To use a camera together with an IMU, the camera-IMU system must be jointly calibrated. The relationship between their respective coordinate frames need to be established, and their timings need to be synchronized. This thesis shows how to automatically perform this calibration and synchronization, without requiring e.g. calibration objects or special motion patterns. In standard structure from motion, the camera trajectory is modeled as discrete poses, with one pose per image. Switching instead to a formulation with a continuous-time camera trajectory provides a natural way to handle rolling shutter distortions, and also to incorporate inertial measurements. To model the continuous-time trajectory, many authors have used splines. The ability for a spline-based trajectory to model the real motion depends on the density of its spline knots. Choosing a too smooth spline results in approximation errors. This thesis proposes a method to estimate the spline approximation error, and use it to better balance camera and IMU measurements, when used in a sensor fusion framework. Also proposed is a way to automatically decide how dense the spline needs to be to achieve a good reconstruction. Another approach to reconstruct a 3D scene is to use a camera that directly measures depth. Some depth cameras, like the well-known Microsoft Kinect, are susceptible to the same rolling shutter effects as normal cameras. This thesis quantifies the effect of the rolling shutter distortion on 3D reconstruction, depending on the amount of motion. It is also shown that a better 3D model is obtained if the depth images are corrected using inertial measurements. Att använda bilder för att återskapa världen omkring oss i tre dimensioner är ett klassiskt problem inom datorseende. Några exempel på användningsområden är inom navigering och kartering för autonoma system, stadsplanering och specialeffekter för film och spel. En vanlig metod för 3D-rekonstruktion är det som kallas ”struktur från rörelse”. Namnet kommer sig av att man avbildar (fotograferar) en miljö från flera olika platser, till exempel genom att flytta kameran. Det är därför något ironiskt att många struktur-från-rörelse-algoritmer får problem om kameran inte är stilla när bilderna tas, exempelvis genom att använda sig av ett stativ. Anledningen är att en kamera i rörelse ger upphov till störningar i bilden vilket ger sämre bildmätningar, och därmed en sämre 3D-rekonstruktion. Ett välkänt exempel är rörelseoskärpa, medan ett annat är kopplat till användandet av en elektronisk rullande slutare. I en kamera med rullande slutare avbildas inte alla pixlar i bilden samtidigt, utan istället rad för rad. Om kameran rör på sig medan bilden tas uppstår därför störningar i bilden som måste tas om hand om för att få en bra rekonstruktion. Den här avhandlingen berör robusta metoder för 3D-rekonstruktion med rörliga kameror. En röd tråd inom arbetet är användandet av en tröghetssensor (IMU). En IMU mäter vinkelhastigheter och accelerationer, och dessa mätningar kan användas för att bestämma hur kameran har rört sig över tid. Kunskap om kamerans rörelse ger möjlighet att korrigera för störningar på grund av den rullande slutaren. Ytterligare en fördel med en IMU är att den ger mätningar även i de fall då en kamera inte kan göra det. Exempel på sådana fall är vid extrem rörelseoskärpa, starkt motljus, eller om det saknas struktur i bilden. Om man vill använda en kamera tillsammans med en IMU så måste dessa kalibreras och synkroniseras: relationen mellan deras respektive koordinatsystem måste bestämmas, och de måste vara överens om vad klockan är. I den här avhandlingen presenteras en metod för att automatiskt kalibrera och synkronisera ett kamera-IMU-system utan krav på exempelvis kalibreringsobjekt eller speciella rörelsemönster. I klassisk struktur från rörelse representeras kamerans rörelse av att varje bild beskrivs med en kamera-pose. Om man istället representerar kamerarörelsen som en tidskontinuerlig trajektoria kan man på ett naturligt sätt hantera problematiken kring rullande slutare. Det gör det också enkelt att införa tröghetsmätningar från en IMU. En tidskontinuerlig kameratrajektoria kan skapas på flera sätt, men en vanlig metod är att använda sig av så kallade splines. Förmågan hos en spline att representera den faktiska kamerarörelsen beror på hur tätt dess knutar placeras. Den här avhandlingen presenterar en metod för att uppskatta det approximationsfel som uppkommer vid valet av en för gles spline. Det uppskattade approximationsfelet kan sedan användas för att balansera mätningar från kameran och IMU:n när dessa används för sensorfusion. Avhandlingen innehåller också en metod för att bestämma hur tät en spline behöver vara för att ge ett gott resultat. En annan metod för 3D-rekonstruktion är att använda en kamera som också mäter djup, eller avstånd. Vissa djupkameror, till exempel Microsoft Kinect, har samma problematik med rullande slutare som vanliga kameror. I den här avhandlingen visas hur den rullande slutaren i kombination med olika typer och storlekar av rörelser påverkar den återskapade 3D-modellen. Genom att använda tröghetsmätningar från en IMU kan djupbilderna korrigeras, vilket visar sig ge en bättre 3D-modell.
Book Synopsis Fusion of Depth and Inertial Sensing for Human Action Recognition by : Chen Chen
Download or read book Fusion of Depth and Inertial Sensing for Human Action Recognition written by Chen Chen and published by . This book was released on 2016 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human action recognition is an active research area benefitting many applications. Example applications include human-computer interaction, assistive-living, rehabilitation, and gaming. Action recognition can be broadly categorized into vision-based and inertial sensor-based. Under realistic operating conditions, it is well known that there are recognition rate limitations when using a single modality sensor due to the fact that no single sensor modality can cope with various situations that occur in practice. The hypothesis addressed in this dissertation is that by using and fusing the information from two differing modality sensors that provide 3D data (a Microsoft Kinect depth camera and a wearable inertial sensor), a more robust human action recognition is achievable. More specifically, effective and computationally efficient features have been devised and extracted from depth images. Both feature-level fusion and decision-level fusion approaches have been investigated for a dual-modality sensing incorporating a depth camera and an inertial sensor. Experimental results obtained indicate that the developed fusion approaches generate higher recognition rates compared to the situations when an individual sensor is used. Moreover, an actual working action recognition system using depth and inertial sensing has been devised which runs in real-time on laptop platforms. In addition, the developed fusion framework has been applied to a medical application.
Book Synopsis Demystifying Human Action Recognition in Deep Learning with Space-Time Feature Descriptors by : Mike Nkongolo
Download or read book Demystifying Human Action Recognition in Deep Learning with Space-Time Feature Descriptors written by Mike Nkongolo and published by GRIN Verlag. This book was released on 2018-02-21 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research Paper (postgraduate) from the year 2018 in the subject Computer Science - Internet, New Technologies, , course: Machine Learning, language: English, abstract: Human Action Recognition is the task of recognizing a set of actions being performed in a video sequence. Reliably and efficiently detecting and identifying actions in video could have vast impacts in the surveillance, security, healthcare and entertainment spaces. The problem addressed in this paper is to explore different engineered spatial and temporal image and video features (and combinations thereof) for the purposes of Human Action Recognition, as well as explore different Deep Learning architectures for non-engineered features (and classification) that may be used in tandem with the handcrafted features. Further, comparisons between the different combinations of features will be made and the best, most discriminative feature set will be identified. In the paper, the development and implementation of a robust framework for Human Action Recognition was proposed. The motivation behind the proposed research is, firstly, the high effectiveness of gradient-based features as descriptors - such as HOG, HOF, and N-Jets - for video-based human action recognition. They are capable of capturing both the salient spatial and temporal information in the video sequences, while removing much of the redundant information that is not pertinent to the action. Combining these features in a hierarchical fashion further increases performance.
Book Synopsis Machine Learning for Vision-Based Motion Analysis by : Liang Wang
Download or read book Machine Learning for Vision-Based Motion Analysis written by Liang Wang and published by Springer. This book was released on 2013-01-02 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.
Book Synopsis Deep Learning Methods for Video-based Human Activity Recognition in Industrial Settings by : Behnoosh Parsa
Download or read book Deep Learning Methods for Video-based Human Activity Recognition in Industrial Settings written by Behnoosh Parsa and published by . This book was released on 2020 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: With increasingly high interest in assistive robots and smart surveillance systems, we need a powerful perception mechanism to be able to describe the events in a scene. However, achieving accurate perception models is not trivial, since, even for one perception task there are unlimited possible scenarios. Hoping to develop analytically driven models seems too optimistic for such systems; hence, Supervised Learning as a sub-field of function approximation has become very popular in robotic perception. Supervised learning is the task of learning a function that maps an input to an output based on example input-output pairs. Scene understanding is even more involved when it comes to solving Human Action Recognition (HAR) problems. In HAR the task is to classify human activities from an image or determine atomic actions composing the activity in a video. In video-based HAR, there are exponentially many ways that humans can perform the same task. Besides, the variety in posture and speed at which people perform activities makes solving HAR tasks even more challenging. Therefore, models should be designed to learn common underlying spatial and temporal properties of human activity to achieve generalizability. This thesis is dedicated to designing perception models for recognizing human actions and determining the ergonomic risk associated with them. Specifically, Part I focus on solving the Human Activity Segmentation (HAS) problem in long videos, which is the task of semantically segmenting long videos into distinct actions in an offline framework. In Part II, we present our designs for solving online-HAR problems to recognize human activities in the observed batch of frames. Since, the performance of computer vision algorithms also depends on the quality and relevance of the training data, in Part I, we introduce a new dataset for an indoor object manipulation task called the University of Washington Indoor Object Manipulation (UW-IOM).
Book Synopsis Human Activity Recognition and Prediction by : Yun Fu
Download or read book Human Activity Recognition and Prediction written by Yun Fu and published by Springer. This book was released on 2016-01-06 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques.
Book Synopsis Deep Learning for Human Motion Analysis by : Natalia Neverova (informaticienne).)
Download or read book Deep Learning for Human Motion Analysis written by Natalia Neverova (informaticienne).) and published by . This book was released on 2020 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research goal of this work is to develop learning methods advancing automatic analysis and interpreting of human motion from different perspectives and based on various sources of information, such as images, video, depth, mocap data, audio and inertial sensors. For this purpose, we propose a several deep neural models and associated training algorithms for supervised classification and semi-supervised feature learning, as well as modelling of temporal dependencies, and show their efficiency on a set of fundamental tasks, including detection, classification, parameter estimation and user verification. First, we present a method for human action and gesture spotting and classification based on multi-scale and multi-modal deep learning from visual signals (such as video, depth and mocap data). Key to our technique is a training strategy which exploits, first, careful initialization of individual modalities and, second, gradual fusion involving random dropping of separate channels (dubbed ModDrop) for learning cross-modality correlations while preserving uniqueness of each modality-specific representation. Moving forward, from 1 to N mapping to continuous evaluation of gesture parameters, we address the problem of hand pose estimation and present a new method for regression on depth images, based on semi-supervised learning using convolutional deep neural networks, where raw depth data is fused with an intermediate representation in the form of a segmentation of the hand into parts. In separate but related work, we explore convolutional temporal models for human authentication based on their motion patterns. In this project, the data is captured by inertial sensors (such as accelerometers and gyroscopes) built in mobile devices. We propose an optimized shift-invariant dense convolutional mechanism and incorporate the discriminatively-trained dynamic features in a probabilistic generative framework taking into account temporal characteristics. Our results demonstrate, that human kinematics convey important information about user identity and can serve as a valuable component of multi-modal authentication systems.
Author :Debarati B Chakraborty Publisher :World Scientific Publishing Company ISBN 13 :9789811227110 Total Pages :0 pages Book Rating :4.2/5 (271 download)
Book Synopsis Granular Video Computing by : Debarati B Chakraborty
Download or read book Granular Video Computing written by Debarati B Chakraborty and published by World Scientific Publishing Company. This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume links the concept of granular computing using deep learning and the Internet of Things to object tracking for video analysis. It describes how uncertainties, involved in the task of video processing, could be handled in rough set theoretic granular computing frameworks. Issues such as object tracking from videos in constrained situations, occlusion/overlapping handling, measuring of the reliability of tracking methods, object recognition and linguistic interpretation in video scenes, and event prediction from videos, are the addressed in this volume. The book also looks at ways to reduce data dependency in the context of unsupervised (without manual interaction/ labeled data/ prior information) training.This book may be used both as a textbook and reference book for graduate students and researchers in computer science, electrical engineering, system science, data science, and information technology, and is recommended for both students and practitioners working in computer vision, machine learning, video analytics, image analytics, artificial intelligence, system design, rough set theory, granular computing, and soft computing.
Book Synopsis Scalable Action Recognition in Continuous Video Streams by : Hamed Pirsiavash
Download or read book Scalable Action Recognition in Continuous Video Streams written by Hamed Pirsiavash and published by . This book was released on 2012 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Activity recognition in video has a variety of applications, including rehabilitation, surveillance, and video retrieval. It is relatively easy for a human to recognize actions in a video once he/she watches it. However, in many applications the videos are very long, eg. in life-logging, and/or we need the real-time detection, eg. in human computer interaction. This motivates us to build computer vision and artificial intelligence algorithms to recognize activities in video sequences automatically. We are addressing several challenges in activity recognition, including (1) computational scalability, (2) spatio-temporal feature extraction, (3) spatio-temporal models, and finally, (4) dataset development. (1) Computational Scalability: We develop ``steerable'' models that parsimoniously represent a large collection of templates with a small number of parameters. This results in local detectors scalable enough for a large number of frames and object/action categories. (2) Spatio-temporal feature extraction: Spatio-temporal feature extraction is difficult for scenes with many moving objects that interact and occlude each other. We tackle this problem using the framework of multi-object tracking and developing linear-time, scalable graph-theoretic algorithms for inference. (3) Spatio-temporal models: Actions exhibit complex temporal structure, such as sub-actions of variable durations and compositional orderings. Much research on action recognition ignores such structure and instead focuses on K-way classification of temporally pre-segmented video clips \cite{poppe2010survey, DBLP:journals/csur/AggarwalR11}. We describe lightweight and efficient grammars that segment a continuous video stream into a hierarchical parse of multiple actions and sub-actions. (4) Dataset development: Finally, in terms of evaluation, video benchmarks are relatively scarce compared to the abundance of image benchmarks. It appears difficult to collect (and annotate) large-scale, unscripted footage of people doing interesting things. We discuss one solution, introducing a new, large-scale benchmark for the problem of detecting activities of daily living (ADL) in first-person camera views.
Book Synopsis Deep Learning for Human Motion Analysis by : Natalia Neverova (informaticienne).)
Download or read book Deep Learning for Human Motion Analysis written by Natalia Neverova (informaticienne).) and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research goal of this work is to develop learning methods advancing automatic analysis and interpreting of human motion from different perspectives and based on various sources of information, such as images, video, depth, mocap data, audio and inertial sensors. For this purpose, we propose a several deep neural models and associated training algorithms for supervised classification and semi-supervised feature learning, as well as modelling of temporal dependencies, and show their efficiency on a set of fundamental tasks, including detection, classification, parameter estimation and user verification. First, we present a method for human action and gesture spotting and classification based on multi-scale and multi-modal deep learning from visual signals (such as video, depth and mocap data). Key to our technique is a training strategy which exploits, first, careful initialization of individual modalities and, second, gradual fusion involving random dropping of separate channels (dubbed ModDrop) for learning cross-modality correlations while preserving uniqueness of each modality-specific representation. Moving forward, from 1 to N mapping to continuous evaluation of gesture parameters, we address the problem of hand pose estimation and present a new method for regression on depth images, based on semi-supervised learning using convolutional deep neural networks, where raw depth data is fused with an intermediate representation in the form of a segmentation of the hand into parts. In separate but related work, we explore convolutional temporal models for human authentication based on their motion patterns. In this project, the data is captured by inertial sensors (such as accelerometers and gyroscopes) built in mobile devices. We propose an optimized shift-invariant dense convolutional mechanism and incorporate the discriminatively-trained dynamic features in a probabilistic generative framework taking into account temporal characteristics. Our results demonstrate, that human kinematics convey important information about user identity and can serve as a valuable component of multi-modal authentication systems.
Book Synopsis Deep Learning Object Detection and Tracking Technology Based on Sensor Fusion of Millimeter-wave Radar/Video and Its Application on Embedded Systems by :
Download or read book Deep Learning Object Detection and Tracking Technology Based on Sensor Fusion of Millimeter-wave Radar/Video and Its Application on Embedded Systems written by and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Activity Recognition in Videos Using Deep Learning by : Mahesh R. Shanbhag
Download or read book Activity Recognition in Videos Using Deep Learning written by Mahesh R. Shanbhag and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatically recognizing activities in a video is a long standing goal of computer vision and artificial intelligence. Recently, breakthroughs in deep learning have revolutionized the field of computer vision and today deep models can solve low-level tasks such as image classification and object detection more accurately than humans and even highly trained (human) experts. However, inferring high-level activities from low-level information such as objects in a video is a difficult task because the objects interacting with humans can be too small or similar activities might be captured at different spatial locations or angles. In this thesis, we propose an effective and efficient supervised learning model for solving this difficult task by leveraging advanced deep learning architectures. Our key idea is to formulate activity recognition as a multi-label classification problem in which the input is a set of frames (a video) and the output is an assignment of most probable labels to the four elements that make up an activity: action, tool, object and source/target at each frame. We begin with a network pre-trained on objects appearing in a large image classification dataset and then modify it with an additional layer that helps us solve the much harder multilabel classification problem. Then, we tune and train this new network to our video data by presenting each labeled frame in the video as input to the network. We train, evaluate and benchmark the model using a popular Cooking activities dataset and also interpret the learned model by visualizing the network at various levels of hierarchy.
Book Synopsis Motion Histograms for Action Recognition Using a Convolutional Neural Network by : David Loïc Chotard
Download or read book Motion Histograms for Action Recognition Using a Convolutional Neural Network written by David Loïc Chotard and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Video recognition of human actions is an important research subject in the field of computer vision. Such solutions are particularly useful in video surveillance to detect potential felonies, but also find applications in other domains such as patient monitoring, video summarization, and video analysis. Even modern video games use action recognition algorithms so that the player becomes more active. The classification of human actions, however, remains a challenging problem due to the large variation in imaging conditions and individual attributes of people performing the action. One of the main difficulty in designing such systems lies in the fact the concept of action is closely related to the one of motion, which is a human impression. Indeed, considering frames independently disregarding the motion is not robust enough as different actions may look similar at a frame level. In this thesis, we use a convolutional neural network that can classify actions taking the motion information into account. Starting from a dataset of labeled actions, we compute the optical flow field that quantifies the motion across every two frames. Based on the flow orientations, we then build a histogram for each action that results in a low dimensionality representation. Actions are thus described as orientation distributions directly related to the motion. We finally use the histograms to train a convolutional neural network that can extract low-level features to increase the classification accuracy. We present results of our method across two benchmark datasets achieving 88.8% accuracy on the UCF Sports dataset, which consist in 13 reference sport actions, and up to 35.7% on the HMDB51 dataset, which contain 51 more complex actions voluntarily including ambiguities and mistakes. In both cases we outperform numerous methods and nearly reach state-of-the-art algorithms. Our method allows for encoding of discriminative features and facilitates action recognition independent of the length of the video. It constitutes a good alternative to non-linear classifiers usually used such as support vector machines.
