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Author: Lu Xia
Publisher:
ISBN:
Category :
Languages : en
Pages : 104
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Book Description
Traditional computer vision algorithms depend on information taken by visible-light cameras. But there are inherent limitations of this data source, e.g. they are sensitive to illumination changes, occlusions and background clutter. Range sensors give us 3D structural information of the scene and it's robust to the change of color and illumination. In this thesis, we present a series of approaches which are developed using the depth information by Kinect to address the issues regarding human detection and action recognition. Taking the depth information, the basic problem we consider is to detect humans in the scene. We propose a model based approach, which is comprised of a 2D head contour detector and a 3D head surface detector. We propose a segmentation scheme to segment the human from the surroundings based on the detection point and extract the whole body of the subject. We also explore the tracking algorithm based on our detection result. The methods are tested on a dataset we collected and present superior results over the existing algorithms. With the detection result, we further studied on recognizing their actions. We present a novel approach for human action recognition with histograms of 3D joint locations (HOJ3D) as a compact representation of postures. We extract the 3D skeletal joint locations from Kinect depth maps using Shotton et al.'s method. The HOJ3D computed from the action depth sequences are reprojected using LDA and then clustered into k posture visual words, which represent the prototypical poses of actions. The temporal evolutions of those visual words are modeled by discrete hidden Markov models (HMMs). In addition, due to the design of our spherical coordinate system and the robust 3D skeleton estimation from Kinect, our method demonstrates significant view invariance on our 3D action dataset. Our dataset is composed of 200 3D sequences of 10 indoor activities performed by 10 individuals in varied views. Our method is real-time and achieves superior results on the challenging 3D action dataset. We also tested our algorithm on the MSR Action3D dataset and our algorithm outperforms existing algorithm on most of the cases.
Author: Lu Xia
Publisher:
ISBN:
Category :
Languages : en
Pages : 104
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Book Description
Traditional computer vision algorithms depend on information taken by visible-light cameras. But there are inherent limitations of this data source, e.g. they are sensitive to illumination changes, occlusions and background clutter. Range sensors give us 3D structural information of the scene and it's robust to the change of color and illumination. In this thesis, we present a series of approaches which are developed using the depth information by Kinect to address the issues regarding human detection and action recognition. Taking the depth information, the basic problem we consider is to detect humans in the scene. We propose a model based approach, which is comprised of a 2D head contour detector and a 3D head surface detector. We propose a segmentation scheme to segment the human from the surroundings based on the detection point and extract the whole body of the subject. We also explore the tracking algorithm based on our detection result. The methods are tested on a dataset we collected and present superior results over the existing algorithms. With the detection result, we further studied on recognizing their actions. We present a novel approach for human action recognition with histograms of 3D joint locations (HOJ3D) as a compact representation of postures. We extract the 3D skeletal joint locations from Kinect depth maps using Shotton et al.'s method. The HOJ3D computed from the action depth sequences are reprojected using LDA and then clustered into k posture visual words, which represent the prototypical poses of actions. The temporal evolutions of those visual words are modeled by discrete hidden Markov models (HMMs). In addition, due to the design of our spherical coordinate system and the robust 3D skeleton estimation from Kinect, our method demonstrates significant view invariance on our 3D action dataset. Our dataset is composed of 200 3D sequences of 10 indoor activities performed by 10 individuals in varied views. Our method is real-time and achieves superior results on the challenging 3D action dataset. We also tested our algorithm on the MSR Action3D dataset and our algorithm outperforms existing algorithm on most of the cases.
Author: Jiang Wang
Publisher: Springer Science & Business Media
ISBN: 331904561X
Category : Computers
Languages : en
Pages : 65
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Book Description
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.
Author: Andrea Fossati
Publisher: Springer Science & Business Media
ISBN: 1447146395
Category : Computers
Languages : en
Pages : 220
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Book Description
The potential of consumer depth cameras extends well beyond entertainment and gaming, to real-world commercial applications. This authoritative text reviews the scope and impact of this rapidly growing field, describing the most promising Kinect-based research activities, discussing significant current challenges, and showcasing exciting applications. Features: presents contributions from an international selection of preeminent authorities in their fields, from both academic and corporate research; addresses the classic problem of multi-view geometry of how to correlate images from different viewpoints to simultaneously estimate camera poses and world points; examines human pose estimation using video-rate depth images for gaming, motion capture, 3D human body scans, and hand pose recognition for sign language parsing; provides a review of approaches to various recognition problems, including category and instance learning of objects, and human activity recognition; with a Foreword by Dr. Jamie Shotton.
Author: Carlos Travieso-Gonzalez
Publisher: BoD – Books on Demand
ISBN: 9535133772
Category : Computers
Languages : en
Pages : 175
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Book Description
Nowadays, the technological advances allow developing many applications on different fields. In this book Motion Tracking and Gesture Recognition, two important fields are shown. Motion tracking is observed by a hand-tracking system for surgical training, an approach based on detection of dangerous situation by the prediction of moving objects, an approach based on human motion detection results and preliminary environmental information to build a long-term context model to describe and predict human activities, and a review about multispeaker tracking on different modalities. On the other hand, gesture recognition is shown by a gait recognition approach using Kinect sensor, a study of different methodologies for studying gesture recognition on depth images, and a review about human action recognition and the details about a particular technique based on a sensor of visible range and with depth information.
Author: Ling Shao
Publisher: Springer
ISBN: 3319086510
Category : Computers
Languages : en
Pages : 313
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Book Description
This book presents an interdisciplinary selection of cutting-edge research on RGB-D based computer vision. Features: discusses the calibration of color and depth cameras, the reduction of noise on depth maps and methods for capturing human performance in 3D; reviews a selection of applications which use RGB-D information to reconstruct human figures, evaluate energy consumption and obtain accurate action classification; presents an approach for 3D object retrieval and for the reconstruction of gas flow from multiple Kinect cameras; describes an RGB-D computer vision system designed to assist the visually impaired and another for smart-environment sensing to assist elderly and disabled people; examines the effective features that characterize static hand poses and introduces a unified framework to enforce both temporal and spatial constraints for hand parsing; proposes a new classifier architecture for real-time hand pose recognition and a novel hand segmentation and gesture recognition system.
Author: Chen Chen
Publisher:
ISBN:
Category : Human activity recognition
Languages : en
Pages : 260
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Book Description
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.
Author: Mohamed Kamel
Publisher: Springer
ISBN: 3642390943
Category : Computers
Languages : en
Pages : 828
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Book Description
This book constitutes the thoroughly refereed proceedings of the 10th International Conference on Image Analysis and Recognition, ICIAR 2013, held in Póvoa do Varzim, Portugal, in June 2013, The 92 revised full papers presented were carefully reviewed and selected from 177 submissions. The papers are organized in topical sections on biometrics: behavioral; biometrics: physiological; classification and regression; object recognition; image processing and analysis: representations and models, compression, enhancement , feature detection and segmentation; 3D image analysis; tracking; medical imaging: image segmentation, image registration, image analysis, coronary image analysis, retinal image analysis, computer aided diagnosis, brain image analysis; cell image analysis; RGB-D camera applications; methods of moments; applications.
Author: Nasser Kehtarnavaz
Publisher: Morgan & Claypool Publishers
ISBN: 1598290533
Category : Technology & Engineering
Languages : en
Pages : 108
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Book Description
This book presents an overview of the guidelines and strategies for transitioning an image or video processing algorithm from a research environment into a real-time constrained environment. Such guidelines and strategies are scattered in the literature of various disciplines including image processing, computer engineering, and software engineering, and thus have not previously appeared in one place. By bringing these strategies into one place, the book is intended to serve the greater community of researchers, practicing engineers, industrial professionals, who are interested in taking an image or video processing algorithm from a research environment to an actual real-time implementation on a resource constrained hardware platform. These strategies consist of algorithm simplifications, hardware architectures, and software methods. Throughout the book, carefully selected representative examples from the literature are presented to illustrate the discussed concepts. After reading the book, the readers are exposed to a wide variety of techniques and tools, which they can then employ to design a real-time image or video processing system.
Author: Srilatha V.
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 7
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Book Description
Trustworthy contextual data of human action recognition of remotely monitored person who requires medical care should be generated to avoid hazardous situation and also to provide ubiquitous services in home-based care. It is difficult for numerous reasons. At first level, the data obtained from heterogeneous source have different level of uncertainty. Second level generated information can be corrupted due to simultaneous operations. In this paper human action recognition can be done based on two different modality consisting of fully featured camera and wearable sensor.
Author: Omar Ghassan Kayal
Publisher:
ISBN:
Category :
Languages : en
Pages : 186
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Book Description
Conventional human action recognition methods use a single light camera to extract all the necessary information needed to perform the recognition. However, the use of a single light camera poses limitations which can not be addressed without a hardware change. In this thesis, we propose a novel approach to the multi camera setup. Our approach utilizes the skeletal pose estimation capabilities of the Microsoft Kinect camera, and uses this estimated pose on the image of the non-depth camera. The approach aims at improving performance of image analysis of multiple camera, which would not be as easy in a typical multiple camera setup. The depth information sharing between the camera is in the form of pose projection, which depends on location awareness between them, where the locations can be found using chessboard pattern calibration techniques. Due to the limitations of pattern calibration, we propose a novel calibration refinement approach to increase the detection distance, and simplify the long calibration process. The two tests performed demonstrate that the pose projection process performs with good accuracy with a successful calibration and good Kinect pose estimation, however not so with a failed one. Three tests were performed to determine the calibration performance. Distance calculations were prone to error with a mean accuracy of 96% under 60cm difference, and dropping drastically beyond that, and a stable orientation calculation with mean accuracy of 97%. Last test also proves that our new refinement approach improves the outcome of the projection significantly with a failed pattern calibration, and allows for almost double the camera difference detection of about 120cm. While the orientation mean calculation accuracy achieved similar results to pattern calibration, the distance was less so at around 92%, however, it did maintain a stable standard deviation, while the pattern calibration increased as distance increased.
