Results 11 - 20
of
203
Gaussian process dynamical models for human motion
- IEEE TRANS. PATTERN ANAL. MACHINE INTELL
, 2008
"... We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensional motion capture data. A GPDM is a latent variable model. It comprises a lowdimensional latent space with associated dynamics, ..."
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Cited by 158 (5 self)
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We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensional motion capture data. A GPDM is a latent variable model. It comprises a lowdimensional latent space with associated dynamics, as well as a map from the latent space to an observation space. We marginalize out the model parameters in closed form by using Gaussian process priors for both the dynamical and the observation mappings. This results in a nonparametric model for dynamical systems that accounts for uncertainty in the model. We demonstrate the approach and compare four learning algorithms on human motion capture data, in which each pose is 50-dimensional. Despite the use of small data sets, the GPDM learns an effective representation of the nonlinear dynamics in these spaces.
Action snippets: How many frames does human action recognition require
- In CVPR
, 2008
"... Visual recognition of human actions in video clips has been an active field of research in recent years. However, most published methods either analyse an entire video and assign it a single action label, or use relatively large lookahead to classify each frame. Contrary to these strategies, human v ..."
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Cited by 156 (2 self)
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Visual recognition of human actions in video clips has been an active field of research in recent years. However, most published methods either analyse an entire video and assign it a single action label, or use relatively large lookahead to classify each frame. Contrary to these strategies, human vision proves that simple actions can be recognised almost instantaneously. In this paper, we present a system for action recognition from very short sequences (“snippets”) of 1–10 frames, and systematically evaluate it on standard data sets. It turns out that even local shape and optic flow for a single frame are enough to achieve ≈ 90% correct recognitions, and snippets of 5-7 frames (0.3-0.5 seconds of video) are enough to achieve a performance similar to the one obtainable with the entire video sequence. 1.
Retrieving actions in movies
"... We address recognition and localization of human actions in realistic scenarios. In contrast to the previous work studying human actions in controlled settings, here we train and test algorithms on real movies with substantial variation of actions in terms of subject appearance, motion, surrounding ..."
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Cited by 149 (7 self)
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We address recognition and localization of human actions in realistic scenarios. In contrast to the previous work studying human actions in controlled settings, here we train and test algorithms on real movies with substantial variation of actions in terms of subject appearance, motion, surrounding scenes, viewing angles and spatio-temporal extents. We introduce a new annotated human action dataset and use it to evaluate several existing methods. We in particular focus on boosted space-time window classifiers and introduce “keyframe priming ” that combines discriminative models of human motion and shape within an action. Keyframe priming is shown to significantly improve the performance of action detection. We present detection results for the action class “drinking ” evaluated on two episodes of the movie “Coffee and Cigarettes”. 1.
Space-time behavior based correlation . . .
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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Event-based analysis of video
- In Proc. CVPR
, 2001
"... Dynamic events can be regarded as long-term temporal objects, which are characterized by spatiotemporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences (possibly of different lengths) based on their behavioral content. This ..."
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Cited by 145 (2 self)
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Dynamic events can be regarded as long-term temporal objects, which are characterized by spatiotemporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences (possibly of different lengths) based on their behavioral content. This measure is non-parametric and can thus handle a wide range of dynamic events. Having an event-based distance measure between sequences, we use it for a variety of tasks, including: (i) event-based search and indexing into long video sequences (for “intelligent fast forward”), (ii) temporal segmentation of long video sequences based on behavioral content, and (iii) clustering events within long video sequence into event-consistent sub-sequences (i.e., into event-consistent “clusters”). These tasks are performed without prior knowledge of the types of events, their models, or their temporal extents. Our simple event representation and associated distance measure supports event-based search and indexing even when only one short example-clip is available. However, when multiple example-clips of the same event are available (either as a result of the clustering process, or supplied manually), these can be used to refine the event representation, the associated distance measure, and accordingly the quality of the detection and clustering process. 1
A system for learning statistical motion patterns
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... permission from the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of th ..."
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Cited by 119 (1 self)
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permission from the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. © 2006 IEEE. Copyright and all rights therein are retained by authors or by other copyright holders. All persons downloading this information are expected to adhere to the terms and constraints invoked by copyright. This document or any part thereof may not be reposted without the explicit permission of the copyright holder. Citation for this copy:
Dynamic Texture Recognition
, 2001
"... Dynamic textures are sequences of images that exhibit some form of temporal stationarity, such as waves, steam, and foliage. We pose the problem of recognizing and classifying dynamic textures in the space of dynamical systems where each dynamic texture is uniquely represented. Since the space is no ..."
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Cited by 114 (7 self)
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Dynamic textures are sequences of images that exhibit some form of temporal stationarity, such as waves, steam, and foliage. We pose the problem of recognizing and classifying dynamic textures in the space of dynamical systems where each dynamic texture is uniquely represented. Since the space is non-linear, a distance between models must be defined. We examine three different distances in the space of autoregressive models and assess their power. 1.
A Survey of Vision-Based Methods for Action Representation, Segmentation and Recognition
, 2011
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Recognition of Human Gaits
, 2001
"... We pose the problem of recognizing different types of human gait in the space of dynamical systems where each gait is represented. Established techniques are employed to track a kinematic model of a human body in motion, and the trajectories of the parameters are used to learn a representation of a ..."
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Cited by 94 (9 self)
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We pose the problem of recognizing different types of human gait in the space of dynamical systems where each gait is represented. Established techniques are employed to track a kinematic model of a human body in motion, and the trajectories of the parameters are used to learn a representation of a dynamical system, which defines a gait. Various types of distance between models are then computed. These computations are non trivial due to the fact that, even for the case of linear systems, the space of canonical realizations is not linear. 1.
