Interpretable Deep Learning Based Approach For The Gait Recognition PDF Download
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| Author | : Nelson Hebert Minaya (Graduate student) |
| Publisher | : |
| Total Pages | : 47 |
| Release | : 2021 |
| Genre | : Biometric identification |
| ISBN | : |
Download Interpretable Deep Learning-based Approach for the Gait Recognition Book in PDF, ePub and Kindle
Abstract: Human gait is a unique behavioral characteristic that can be used to recognize individuals. In recent years, the capture of gait information has become a common practice due to the advancement and accessibility of wearable devices that allow to collect it as continuous time-series. Recognizing people by processing this type of gait data has become a topic of research that looks for methods with enough high accuracy that would enable the use of gait for biometric identification. This work addresses the problem of user identification and recognition from collected multi-modal time-series gait information. The recognition problem has two different settings: the first one is closed-set recognition, whereby all testing classes are known at the time of training, and the other one is open-set recognition where unknown classes that were not in the training phase can emerge during testing. This work addresses both settings by proposing frameworks for each one. The inputs for the proposed frameworks are unit steps obtained by segmenting the multi-modal time series collected from individuals wearing a smart insole device.
| Author | : James Eric Mason |
| Publisher | : Springer |
| Total Pages | : 247 |
| Release | : 2016-02-04 |
| Genre | : Technology & Engineering |
| ISBN | : 3319290886 |
Download Machine Learning Techniques for Gait Biometric Recognition Book in PDF, ePub and Kindle
This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF. This book · introduces novel machine-learning-based temporal normalization techniques · bridges research gaps concerning the effect of footwear and stepping speed on footstep GRF-based person recognition · provides detailed discussions of key research challenges and open research issues in gait biometrics recognition · compares biometrics systems trained and tested with the same footwear against those trained and tested with different footwear
| Author | : Matteo Zago |
| Publisher | : Frontiers Media SA |
| Total Pages | : 328 |
| Release | : 2021-03-04 |
| Genre | : Science |
| ISBN | : 2889665615 |
Download Machine Learning Approaches to Human Movement Analysis Book in PDF, ePub and Kindle
| Author | : Nagender Kumar Suryadevara |
| Publisher | : Apress |
| Total Pages | : 182 |
| Release | : 2021-04-02 |
| Genre | : Computers |
| ISBN | : 9781484268421 |
Download Beginning Machine Learning in the Browser Book in PDF, ePub and Kindle
Apply Artificial Intelligence techniques in the browser or on resource constrained computing devices. Machine learning (ML) can be an intimidating subject until you know the essentials and for what applications it works. This book takes advantage of the intricacies of the ML processes by using a simple, flexible and portable programming language such as JavaScript to work with more approachable, fundamental coding ideas. Using JavaScript programming features along with standard libraries, you'll first learn to design and develop interactive graphics applications. Then move further into neural systems and human pose estimation strategies. For training and deploying your ML models in the browser, TensorFlow.js libraries will be emphasized. After conquering the fundamentals, you'll dig into the wilderness of ML. Employ the ML and Processing (P5) libraries for Human Gait analysis. Building up Gait recognition with themes, you'll come to understand a variety of ML implementation issues. For example, you’ll learn about the classification of normal and abnormal Gait patterns. With Beginning Machine Learning in the Browser, you’ll be on your way to becoming an experienced Machine Learning developer. What You’ll Learn Work with ML models, calculations, and information gathering Implement TensorFlow.js libraries for ML models Perform Human Gait Analysis using ML techniques in the browser Who This Book Is For Computer science students and research scholars, and novice programmers/web developers in the domain of Internet Technologies
| Author | : Simone Tassani |
| Publisher | : Frontiers Media SA |
| Total Pages | : 120 |
| Release | : 2024-06-06 |
| Genre | : Technology & Engineering |
| ISBN | : 283255010X |
Download Cross-Disciplinary Approaches to Characterize Gait and Posture Disturbances in Aging and Related Diseases, volume II Book in PDF, ePub and Kindle
Aging introduces disturbances to the gait and posture of individuals. Such alterations can originate or result from a wide range of causes making it challenging to understand when modifications are related to pathological or physiological causes. Many disciplines come together to perform this kind of analysis (e.g. computational and experimental mechanics, image processing, medicine, biology, physiology, machine learning, and data science). Gait analysis is particularly sensitive to the interactions of different disciplines. This technique allows the study of human movements, but only through a multidisciplinary approach, it is possible to infer relations of causation. Narrow studies focusing on specific techniques are important to develop the fundamental tools required to study movement. In recent years, significant methodological advancements have been independently made in these fields. However, to cross the borders of current science and develop consistent results any new study needs to set interdisciplinary goals. An inclusive approach merging multiple aspects would be key in targeting pharmacological or rehabilitation interventions and improving patient care as a whole.
| Author | : Thomas Moeslund |
| Publisher | : MDPI |
| Total Pages | : 300 |
| Release | : 2020-06-17 |
| Genre | : Technology & Engineering |
| ISBN | : 3039362283 |
Download Statistical Machine Learning for Human Behaviour Analysis Book in PDF, ePub and Kindle
This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.
| Author | : Hiroshi Ishikawa |
| Publisher | : Springer Nature |
| Total Pages | : 757 |
| Release | : 2021-02-24 |
| Genre | : Computers |
| ISBN | : 3030695352 |
Download Computer Vision – ACCV 2020 Book in PDF, ePub and Kindle
The six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.* The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; segmentation and grouping Part II: low-level vision, image processing; motion and tracking Part III: recognition and detection; optimization, statistical methods, and learning; robot vision Part IV: deep learning for computer vision, generative models for computer vision Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis Part VI: applications of computer vision; vision for X; datasets and performance analysis *The conference was held virtually.
| Author | : Harish Garg |
| Publisher | : Elsevier |
| Total Pages | : 402 |
| Release | : 2023-07-20 |
| Genre | : Computers |
| ISBN | : 0443194149 |
Download Deep Learning in Personalized Healthcare and Decision Support Book in PDF, ePub and Kindle
Deep Learning in Personalized Healthcare and Decision Support discusses the potential of deep learning technologies in the healthcare sector. The book covers the application of deep learning tools and techniques in diverse areas of healthcare, such as medical image classification, telemedicine, clinical decision support system, clinical trials, electronic health records, precision medication, Parkinson disease detection, genomics, and drug discovery. In addition, it discusses the use of DL for fraud detection and internet of things. This is a valuable resource for researchers, graduate students and healthcare professionals who are interested in learning more about deep learning applied to the healthcare sector. Although there is an increasing interest by clinicians and healthcare workers, they still lack enough knowledge to efficiently choose and make use of technologies currently available. This book fills that knowledge gap by bringing together experts from technology and clinical fields to cover the topics in depth. Discusses the application of deep learning in several areas of healthcare, including clinical trials, telemedicine and health records management Brings together experts in the intersection of deep learning, medicine, healthcare and programming to cover topics in an interdisciplinary way Uncovers the stakes and possibilities involved in realizing personalized healthcare services through efficient and effective deep learning technologies
| Author | : Masoud Dorrikhteh |
| Publisher | : |
| Total Pages | : |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
Download A Deep-Learning Approach for Marker-less Stride Parameters Analysis with Two Cameras Book in PDF, ePub and Kindle
Human gait analysis is an essential indicator for physical and neuroglial health of an individual. Recent developments in deep-learning approaches to computer vision make possible new techniques for body segment and joint detection from photos and video frames. In this thesis, we propose a deep learning approach for non-invasive video-based gait analysis using two RGB cameras that would be suitable for routine gait monitoring in senior care and rehabilitation centers. Due to modularity and the low cost of implementation, it is considered an affordable solution for such centers. Furthermore, since the solution does not require any markers or sensors to be worn, it is a pervasive and easy method for daily usage. Our proposed deep-learning approach starts by calibrating both the intrinsic and extrinsic parameters of the cameras. Next, video streams captured from two RGB cameras are used as input, and OpenPose and HyperPose deep-learning frameworks are used to localize the main body key points, including the joints and skeleton based on Body 25 and COCO models, respectively. The 2D parameter outputs from the frameworks are triangulated into 3D vector spaces for further analysis. In order to reduce the noises in our data, we applied median and dual pass butter worth filters to the data. Finally gait parameters has been extracted measured and compared to the manually evaluated ground truth data which has been capture via manual measurement of a domain expert. The approach was evaluated in a laboratory setting similar to an institutional hallway in five types of trials: walking back and forth in a straight line while turning out of frame, walking back and forth in a straight line while turning in frame, circular walking, walking with a cane and a walker. The method brings promising results compared to more expensive and restrictive approaches that use up to 16 cameras and require markers or sensors.
| Author | : Alexander Matasa |
| Publisher | : |
| Total Pages | : 58 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
Download A Deep Learning Approach for Learning Human Gait Signature Book in PDF, ePub and Kindle
With advancements in biometric securities, focus has increased on utilizing gait as a means of recognition. Gait describes the unique walking pattern present in humans and has shown promising results in person re-identification tasks. Unlike other biometric features, gait is unique in that it is a subconscious behavior minimizing the risk of purposeful obfuscation. In this research, we first cover supervised approaches showing that current methods fail to learn a unique signature that describes the motion of a subject. Rather they extract frame-based feature information which is then aggregated. While these methods have shown to be effective, they do not solve the underlying problem of trying to extract a gait signature. In this research, a novel approach is proposed that utilizes motion information to extract a true gait signature. Utilizing the repetitive nature of body part motion, we argue that since each subject has a unique gait, they will also have a unique repetition pattern. From a walking sequence, we are able to extract a frame-based similarity matrix that effectively shows a unique repetitive pattern. We further explore this idea using unsupervised learning and show that this unique repetition pattern performs well in both multi-view and cross-covariate scenarios. Currently there are no unsupervised methods for gait recognition, so this foundational work acts as a guideline for future research and evaluation. In addition, the proposed unsupervised method outperforms many supervised methods.
