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Learning Structured Models For Human Actions And Poses
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Book Synopsis Learning Structured Models for Human Actions and Poses by : Yang Wang
Download or read book Learning Structured Models for Human Actions and Poses written by Yang Wang and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A grand challenge of computer vision is to enable machines to "see people''. A solution to this challenge will enable numerous applications in various fields, e.g., security, surveillance, entertainment, human computer interaction, bio-mechanics, etc. This dissertation focus on two problems in the general area of "looking at people"', Human pose estimation and Human action recognition. The first problem is to identify the body parts of a person from a still image. The second problem is to recognize the actions of the person from a video sequence. We formulate the solutions to these problems as learning Structured models. In particular, we propose models and algorithms to address the following structures: (1) human pose estimation as structured output problem. We propose a boosted multiple tree model for modeling the spatial and occlusion constraints between human body parts; (2) temporal structure in human action recognition. We present two models based on the "bag-of-words" representation to capture the temporal structures of video sequences; (3) human action recognition as classification with hidden structures. We develop a model based on the hidden conditional random field to recognize human actions. We also propose a max-margin learning method for training the model. The learning method is general enough to be applied in many other applications in computer vision, even other areas in computer science.
Book Synopsis Spatial-Temporal Hierarchical Model for Joint Learning and Inference of Human Action and Pose by : Xiaohan Nie
Download or read book Spatial-Temporal Hierarchical Model for Joint Learning and Inference of Human Action and Pose written by Xiaohan Nie and published by . This book was released on 2017 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the community of computer vision, human pose estimation and human action recognition are two classic and also of particular important tasks. They always serve as basic preprocessing steps for other high-level tasks such as group activity analysis, visual search and human identication and they are also widely used as key components in many real applications such as intelligent surveillance system and human-computer interaction based system. The two tasks are closely related for understanding human motion, most methods, however, learn separate models and combine them sequentially. In this dissertation, we build systems for pursuing a unied framework to integrate training and inference of human pose estimation and action recognition in a spatial-temporal And-Or Graph (ST-AOG) representation. Particularly, we study dierent ways to achieve this goal: (1) A two-level And-Or Tree structure is utilized for representing action as animated pose template (APT). Each action is a sequence of moving pose templates with transition probabilities. Each Pose template consists of a shape template represented by an And-node capturing part appearance, and a motion template represented by an Or-node capturing part motions. The transitions between moving pose templates are governed in a Hidden Markov Model. The part locations, pose types and action labels are estimated together in inference. (2) In order to tackle actions from unknown and unseen views we present a multi-view spatial-temporal And-Or Graph (MST-AOG) for cross-view action recognition. As a compositional model, the MST-AOG compactly represents the hierarchical combinatorial structures of cross-view actions by explicitly modeling the geometry, appearance and motion variations. The model training takes advantage of the 3D human skeleton data obtained from Kinect cameras to avoid annotating video frames. The ecient inference enables action recognition from novel views. A new Multi-view Action3D dataset has been created and released. (3) To further represent part, pose and action jointly and improve performance, we represent action at three scales by a ST-AOG model. Each action is decomposed into poses which are further divided into mid-level spatial-temporal parts (ST-parts) and then parts. The hierarchical model structure captures the geometric and appearance variations of pose at each frame. The lateral connections between ST-parts at adjacent frames capture the action-specic motions. The model parameters at three scales are learned discriminatively and dynamic programming is utilized for ecient inference. The experiments demonstrate the large benet of joint modeling of the two tasks. (4) The last but not the least, we study a novel framework for full-body 3D human pose estimation which is a essential task for human attention recognition, robot-based human action prediction and interaction. We build a two-level hierarchy of Long Short-Term Memory (LSTM) network with tree-structure to predict the depth on 2D human joints and then reconstruct the 3D pose. Our two-level model utilizes two cues for depth prediction: 1) the global features from 2D skeleton. 2) the local features from image patches of body parts.
Book Synopsis e-Learning, e-Education, and Online Training by : Guan Gui
Download or read book e-Learning, e-Education, and Online Training written by Guan Gui and published by Springer Nature. This book was released on 2024-01-13 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: This four-volume set constitutes the post-conference proceedings of the 9th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2023, held in Yantai, China, during August 17-18, 2023. The 104 full papers presented were selected from 260 submissions. The papers reflect the evolving landscape of education in the digital age. They were organized in topical sections as follows: IT promoted teaching platforms and systems; AI based educational modes and methods; automatic educational resource processing; educational information evaluation.
Book Synopsis Gesture Recognition by : Sergio Escalera
Download or read book Gesture Recognition written by Sergio Escalera and published by Springer. This book was released on 2017-07-19 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a selection of chapters, written by leading international researchers, related to the automatic analysis of gestures from still images and multi-modal RGB-Depth image sequences. It offers a comprehensive review of vision-based approaches for supervised gesture recognition methods that have been validated by various challenges. Several aspects of gesture recognition are reviewed, including data acquisition from different sources, feature extraction, learning, and recognition of gestures.
Book Synopsis Recognizing Simple Human Actions by Exploiting Regularities in Pose Sequences by : Nikola B. Otašević
Download or read book Recognizing Simple Human Actions by Exploiting Regularities in Pose Sequences written by Nikola B. Otašević and published by . This book was released on 2013 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: The human visual system represents a very complex and important part of brain activity, occupying a very significant portion of the cortex resources. It enables us to see colors, detect motion, perceive dimensions and distance. It enables us to solve a very wide range of problems such as image segmentation, object tracking, as well as object and activity recognition. We perform these tasks so easily that we are not even aware of their enormous complexity. How do we do that? This question has motivated decades of research in the field of computer vision. In this thesis, I make a contribution toward solving the particular problem of visionbased human-action recognition by exploiting the compositional nature of simple actions such as running, walking or bending. Noting that simple actions consist of a series of atomic movements and can be represented as a structured sequence of poses, I designed and implemented a system that learns a model of actions based on human-pose classification from a single frame and from a model of transitions between poses through time. The system comprises three parts. The first part is the pose classifier that is capable of inferring a pose from a single frame. Its role is to take as input an image and give its best estimate of the pose in that image. The second part is a hidden Markov model of the transitions between poses. I exploit structural constraints in human motion to build a model that corrects some of the errors made by the independent single-frame pose classifier. Finally, in the third part, the corrected sequence of poses is used to recognize action based on the frequency of pose patterns, the transitions between the poses and hidden Markov models of individual actions. I demonstrate and test my system on the public KTH dataset, which contains examples of running, walking, jogging, boxing, handclapping, and handwaving, as well as on a new dataset, which contains examples of not only running and walking, but also jumping, crouching, crawling, kicking a ball, passing a basketball, and shooting a basketball. On these datasets, my system exhibits 91% action recognition recall rate.
Book Synopsis Computer Vision – ECCV 2012 by : Andrew Fitzgibbon
Download or read book Computer Vision – ECCV 2012 written by Andrew Fitzgibbon and published by Springer. This book was released on 2012-09-26 with total page 901 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.
Book Synopsis Computer Vision -- ECCV 2014 by : David Fleet
Download or read book Computer Vision -- ECCV 2014 written by David Fleet and published by Springer. This book was released on 2014-08-14 with total page 855 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.
Book Synopsis Probabilistic Graphical Models for Computer Vision by : Qiang Ji
Download or read book Probabilistic Graphical Models for Computer Vision written by Qiang Ji and published by Academic Press. This book was released on 2019-11 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and teaches how to develop the PGM model from training data. This book discusses PGMs and their significance in the context of solving computer vision problems, giving the basic concepts, definitions and properties. It also provides a comprehensive introduction to well-established theories for different types of PGMs, including both directed and undirected PGMs, such as Bayesian Networks, Markov Networks and their variants. Discusses PGM theories and techniques with computer vision examples Focuses on well-established PGM theories that are accompanied by corresponding pseudocode for computer vision Includes an extensive list of references, online resources and a list of publicly available and commercial software Covers computer vision tasks, including feature extraction and image segmentation, object and facial recognition, human activity recognition, object tracking and 3D reconstruction
Book Synopsis Computational Intelligence Applications in Business Intelligence and Big Data Analytics by : Vijayan Sugumaran
Download or read book Computational Intelligence Applications in Business Intelligence and Big Data Analytics written by Vijayan Sugumaran and published by CRC Press. This book was released on 2017-06-26 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.
Book Synopsis Computer Vision – ECCV 2016 by : Bastian Leibe
Download or read book Computer Vision – ECCV 2016 written by Bastian Leibe and published by Springer. This book was released on 2016-09-16 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action, activity and tracking; 3D; and 9 poster sessions.
Book Synopsis Computer Vision -- ECCV 2012. Workshops and Demonstrations by : Andrea Fusiello
Download or read book Computer Vision -- ECCV 2012. Workshops and Demonstrations written by Andrea Fusiello and published by Springer. This book was released on 2012-09-26 with total page 703 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three volume set LNCS 7583, 7584 and 7585 comprises the Workshops and Demonstrations which took place in connection with the European Conference on Computer Vision, ECCV 2012, held in Firenze, Italy, in October 2012. The total of 179 workshop papers and 23 demonstration papers was carefully reviewed and selected for inclusion in the proceedings. They where held at workshops with the following themes: non-rigid shape analysis and deformable image alignment; visual analysis and geo-localization of large-scale imagery; Web-scale vision and social media; video event categorization, tagging and retrieval; re-identification; biological and computer vision interfaces; where computer vision meets art; consumer depth cameras for computer vision; unsolved problems in optical flow and stereo estimation; what's in a face?; color and photometry in computer vision; computer vision in vehicle technology: from earth to mars; parts and attributes; analysis and retrieval of tracked events and motion in imagery streams; action recognition and pose estimation in still images; higher-order models and global constraints in computer vision; information fusion in computer vision for concept recognition; 2.5D sensing technologies in motion: the quest for 3D; benchmarking facial image analysis technologies.
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 2015-12-23 with total page 179 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 Computer Vision – ECCV 2018 by : Vittorio Ferrari
Download or read book Computer Vision – ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-05 with total page 880 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
Book Synopsis Visual Analysis of Humans by : Thomas B. Moeslund
Download or read book Visual Analysis of Humans written by Thomas B. Moeslund and published by Springer Science & Business Media. This book was released on 2011-10-08 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique text/reference provides a coherent and comprehensive overview of all aspects of video analysis of humans. Broad in coverage and accessible in style, the text presents original perspectives collected from preeminent researchers gathered from across the world. In addition to presenting state-of-the-art research, the book reviews the historical origins of the different existing methods, and predicts future trends and challenges. Features: with a Foreword by Professor Larry Davis; contains contributions from an international selection of leading authorities in the field; includes an extensive glossary; discusses the problems associated with detecting and tracking people through camera networks; examines topics related to determining the time-varying 3D pose of a person from video; investigates the representation and recognition of human and vehicular actions; reviews the most important applications of activity recognition, from biometrics and surveillance, to sports and driver assistance.
Book Synopsis Learning Human Activities and Poses with Interconnected Data Sources by : Chao-Yeh Chen
Download or read book Learning Human Activities and Poses with Interconnected Data Sources written by Chao-Yeh Chen and published by . This book was released on 2016 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding human actions and poses in images or videos is a challenging problem in computer vision. There are different topics related to this problem such as action recognition, pose estimation, human-object interaction, and activity detection. Knowledge of actions and poses could benefit many applications, including video search, surveillance, auto-tagging, event detection, and human-computer interfaces. To understand humans' actions and poses, we need to address several challenges. First, humans are able to perform an enormous amount of poses. For example, simply to move forward, we can do crawling, walking, running, and sprinting. These poses all look different and require examples to cover these variations. Second, the appearance of a person's pose changes when looking from different viewing angles. The learned action model needs to cover the variations from different views. Third, many actions involve interactions between people and other objects, so we need to consider the appearance change corresponding to that object as well. Fourth, collecting such data for learning is difficult and expensive. Last, even if we can learn a good model for an action, to localize when and where the action happens in a long video remains a difficult problem due to the large search space. My key idea to alleviate these obstacles in learning humans' actions and poses is to discover the underlying patterns that connect the information from different data sources. Why will there be underlying patterns? The intuition is that all people share the same articulated physical structure. Though we can change our pose, there are common regulations that limit how our pose can be and how it can move over time. Therefore, all types of human data will follow these rules and they can serve as prior knowledge or regularization in our learning framework. If we can exploit these tendencies, we are able to extract additional information from data and use them to improve learning of humans' actions and poses. In particular, we are able to find patterns for how our pose could vary over time, how our appearance looks in a specific view, how our pose is when we are interacting with objects with certain properties, and how part of our body configuration is shared across different poses. If we could learn these patterns, they can be used to interconnect and extrapolate the knowledge between different data sources. To this end, I propose several new ways to connect human activity data. First, I show how to connect snapshot images and videos by exploring the patterns of how our pose could change over time. Building on this idea, I explore how to connect humans' poses across multiple views by discovering the correlations between different poses and the latent factors that affect the viewpoint variations. In addition, I consider if there are also patterns connecting our poses and nearby objects when we are interacting with them. Furthermore, I explore how we can utilize the predicted interaction as a cue to better address existing recognition problems including image re-targeting and image description generation. Finally, after learning models effectively incorporating these patterns, I propose a robust approach to efficiently localize when and where a complex action happens in a video sequence. The variants of my proposed approaches offer a good trade-off between computational cost and detection accuracy. My thesis exploits various types of underlying patterns in human data. The discovered structure is used to enhance the understanding of humans' actions and poses. By my proposed methods, we are able to 1) learn an action with very few snapshots by connecting them to a pool of label-free videos, 2) infer the pose for some views even without any examples by connecting the latent factors between different views, 3) predict the location of an object that a person is interacting with independent of the type and appearance of that object, then use the inferred interaction as a cue to improve recognition, and 4) localize an action in a complex long video. These approaches improve existing frameworks for understanding humans' actions and poses without extra data collection cost and broaden the problems that we can tackle.
Book Synopsis Artificial Intelligence for Sustainable Development by : Anandakumar Haldorai
Download or read book Artificial Intelligence for Sustainable Development written by Anandakumar Haldorai and published by Springer Nature. This book was released on with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Wireless Algorithms, Systems, and Applications by : Zhe Liu
Download or read book Wireless Algorithms, Systems, and Applications written by Zhe Liu and published by Springer Nature. This book was released on 2021-09-08 with total page 635 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 12937 - 12939 constitutes the proceedings of the 16th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2021, which was held during June 25-27, 2021. The conference took place in Nanjing, China.The 103 full and 57 short papers presented in these proceedings were carefully reviewed and selected from 315 submissions. The following topics are covered in Part I of the set: network protocols, signal processing, wireless telecommunication systems, blockchain, IoT and edge computing, artificial intelligence, computer security, distributed computer systems, machine learning, and others.
