Learning Transferable Distance Functions For Human Action Recognition And Detection
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| Author | : Hongbin Zha |
| Publisher | : Springer |
| Total Pages | : 742 |
| Release | : 2010-05-09 |
| Genre | : Computers |
| ISBN | : 364212304X |
It givesus greatpleasureto presentthe proceedings of the 9th Asian Conference on Computer Vision (ACCV 2009), held in Xi’an, China, in September 2009. This was the ?rst ACCV conference to take place in mainland China. We received a total of 670 full submissions, which is a new record in the ACCV series. Overall, 35 papers were selected for oral presentation and 131 as posters, yielding acceptance rates of 5.2% for oral, 19.6% for poster, and 24.8% in total. In the paper reviewing, we continued the tradition of previous ACCVsbyconductingtheprocessinadouble-blindmanner.Eachofthe33Area Chairs received a pool of about 20 papers and nominated a number of potential reviewers for each paper. Then, Program Committee Chairs allocated at least three reviewers to each paper, taking into consideration any con?icts of interest and the balance of loads. Once the reviews were ?nished, the Area Chairs made summaryreportsforthepapersintheirpools,basedonthereviewers’comments and on their own assessments of the papers.
| Author | : Liang Wang |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 377 |
| Release | : 2010-11-18 |
| Genre | : Computers |
| ISBN | : 0857290576 |
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.
| Author | : Hervé Bourlard |
| Publisher | : EPFL Press |
| Total Pages | : 369 |
| Release | : 2021-04-15 |
| Genre | : Reference |
| ISBN | : 2940222711 |
In the past twenty years, computers and networks have gained a prominent role in supporting human communications. This book presents recent research in multimodal information processing, which demonstrates that computers can achieve more than what telephone calls or videoconferencing can do. The book offers a snapshot of current capabilities for the analysis of human communications in several modalities – audio, speech, language, images, video, and documents – and for accessing this information interactively. The book has a clear application goal, which is the capture, automatic analysis, storage, and retrieval of multimodal signals from human interaction in meetings. This goal provides a controlled experimental framework and helps generating shared data, which is required for methods based on machine learning. This goal has shaped the vision of the contributors to the book and of many other researchers cited in it. It has also received significant long-term support through a series of projects, including the Swiss National Center of Competence in Research (NCCR) in Interactive Multimodal Information Management (IM2), to which the contributors to the book have been connected.
| Author | : Vittorio Murino |
| Publisher | : Academic Press |
| Total Pages | : 440 |
| Release | : 2017-04-18 |
| Genre | : Computers |
| ISBN | : 0128092807 |
Group and Crowd Behavior for Computer Vision provides a multidisciplinary perspective on how to solve the problem of group and crowd analysis and modeling, combining insights from the social sciences with technological ideas in computer vision and pattern recognition. The book answers many unresolved issues in group and crowd behavior, with Part One providing an introduction to the problems of analyzing groups and crowds that stresses that they should not be considered as completely diverse entities, but as an aggregation of people. Part Two focuses on features and representations with the aim of recognizing the presence of groups and crowds in image and video data. It discusses low level processing methods to individuate when and where a group or crowd is placed in the scene, spanning from the use of people detectors toward more ad-hoc strategies to individuate group and crowd formations. Part Three discusses methods for analyzing the behavior of groups and the crowd once they have been detected, showing how to extract semantic information, predicting/tracking the movement of a group, the formation or disaggregation of a group/crowd and the identification of different kinds of groups/crowds depending on their behavior. The final section focuses on identifying and promoting datasets for group/crowd analysis and modeling, presenting and discussing metrics for evaluating the pros and cons of the various models and methods. This book gives computer vision researcher techniques for segmentation and grouping, tracking and reasoning for solving group and crowd modeling and analysis, as well as more general problems in computer vision and machine learning. - Presents the first book to cover the topic of modeling and analysis of groups in computer vision - Discusses the topics of group and crowd modeling from a cross-disciplinary perspective, using social science anthropological theories translated into computer vision algorithms - Focuses on group and crowd analysis metrics - Discusses real industrial systems dealing with the problem of analyzing groups and crowds
| Author | : Herve Bourlard |
| Publisher | : CRC Press |
| Total Pages | : 367 |
| Release | : 2014-01-07 |
| Genre | : Science |
| ISBN | : 1482212137 |
This book provides a synthesis of the multifaceted field of interactive multimodal information management. The subjects treated include spoken language processing, image and video processing, document and handwriting analysis, identity information and interfaces. The book concludes with an overview of the highlights of the progress of the field dur
| Author | : Qiang Yang |
| Publisher | : Cambridge University Press |
| Total Pages | : 394 |
| Release | : 2020-02-13 |
| Genre | : Computers |
| ISBN | : 1108860087 |
Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.
| Author | : Albert Ali Salah |
| Publisher | : Springer |
| Total Pages | : 166 |
| Release | : 2011-11-10 |
| Genre | : Computers |
| ISBN | : 3642254462 |
This book constitutes the refereed proceedings of the Second International Workshop on Human Behavior Understanding, HBU 2011, held in Amsterdam, The Netherlands, in November 2011, in conjunction with AmI-11, the International Joint Conference on Ambient Intelligence. The 13 revised full papers presented together with 2 keynote talks and one summarizing paper were carefully reviewed and selected from 32 submissions. The papers are organized in topical sections on analysis of human actions and activities, face and gesture analysis, persuasive technologies, and social interactions.
| Author | : Albert Ali Salah |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 166 |
| Release | : 2011-11-18 |
| Genre | : Computers |
| ISBN | : 3642254454 |
This book constitutes the refereed proceedings of the Second International Workshop on Human Behavior Understanding, HBU 2011, held in Amsterdam, The Netherlands, in November 2011, in conjunction with AmI-11, the International Joint Conference on Ambient Intelligence. The 13 revised full papers presented together with 2 keynote talks and one summarizing paper were carefully reviewed and selected from 32 submissions. The papers are organized in topical sections on analysis of human actions and activities, face and gesture analysis, persuasive technologies, and social interactions.
| Author | : Shaogang Gong |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 358 |
| Release | : 2011-05-26 |
| Genre | : Computers |
| ISBN | : 0857296701 |
This book presents a comprehensive treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives. Topics: covers learning-group activity models, unsupervised behaviour profiling, hierarchical behaviour discovery, learning behavioural context, modelling rare behaviours, and “man-in-the-loop” active learning; examines multi-camera behaviour correlation, person re-identification, and “connecting-the-dots” for abnormal behaviour detection; discusses Bayesian information criterion, Bayesian networks, “bag-of-words” representation, canonical correlation analysis, dynamic Bayesian networks, Gaussian mixtures, and Gibbs sampling; investigates hidden conditional random fields, hidden Markov models, human silhouette shapes, latent Dirichlet allocation, local binary patterns, locality preserving projection, and Markov processes; explores probabilistic graphical models, probabilistic topic models, space-time interest points, spectral clustering, and support vector machines.
| Author | : Sourav De |
| Publisher | : CRC Press |
| Total Pages | : 125 |
| Release | : 2020-09-28 |
| Genre | : Computers |
| ISBN | : 0429589999 |
Human Action Recognition is a challenging area presently. The vigor of research effort directed towards this domain is self indicative of this. With the ever-increasing involvement of Computational Intelligence in our day to day applications, the necessity of human activity recognition has been able to make its presence felt to the concerned research community. The primary drive of such an effort is to equip the computing system capable of recognizing and interpreting human activities from posture, pose, gesture, facial expression etc. The intent of human activity recognition is a formidable component of cognitive science in which researchers are actively engaged of late. Features: A systematic overview of the state-of-the-art in computational intelligence techniques for human action recognition. Emphasized on different intelligent techniques to recognize different human actions. Discussed about the automation techniques to handle human action recognition. Recent research results and some pointers to future advancements in this arena. In the present endeavour the editors intend to come out with a compilation that reflects the concerns of relevant research community. The readers would be able to come across some of the latest findings of active researchers of the concerned field. It is anticipated that this treatise shall be useful to the readership encompassing students at undergraduate and postgraduate level, researchers active as well as aspiring, not to speak of the senior researchers.
