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45,481
HUMAN ACTION RECOGNITION USING LOCAL SPACE TIME FEATURES AND ADABOOST SVM
"... Human action recognition has a wide range of promising applications like video surveillance, intelligent interface, and video retrieval. The objective of this project is to recognize and annotate different human activities present in digital videos taken from both constrained and unconstrained envir ..."
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environment. This work extended the use of the techniques existing in object recognition in 2D images to video sequences by determining the Spatio-temporal Interest points using the extension of Harris operator in the time dimension. Features descriptors are computed on the cuboids around these interest
Human Action Recognition and Localization in Video Using Structured Learning of Local Space-Time Features
- in Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance
, 2010
"... This paper presents a unified framework for human ac-tion classification and localization in video using structured learning of local space-time features. Each human action class is represented by a set of its own compact set of lo-cal patches. In our approach, we first use a discriminative hierarch ..."
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Cited by 4 (0 self)
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This paper presents a unified framework for human ac-tion classification and localization in video using structured learning of local space-time features. Each human action class is represented by a set of its own compact set of lo-cal patches. In our approach, we first use a discriminative
Recognizing human actions: A local SVM approach
- In ICPR
, 2004
"... Local space-time features capture local events in video and can be adapted to the size, the frequency and the velocity of moving patterns. In this paper we demonstrate how such features can be used for recognizing complex motion patterns. We construct video representations in terms of local space-ti ..."
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Cited by 758 (20 self)
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Local space-time features capture local events in video and can be adapted to the size, the frequency and the velocity of moving patterns. In this paper we demonstrate how such features can be used for recognizing complex motion patterns. We construct video representations in terms of local space-time
Space-time Interest Points
- IN ICCV
, 2003
"... Local image features or interest points provide compact and abstract representations of patterns in an image. In this paper, we propose to extend the notion of spatial interest points into the spatio-temporal domain and show how the resulting features often reflect interesting events that can be use ..."
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Cited by 819 (21 self)
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Local image features or interest points provide compact and abstract representations of patterns in an image. In this paper, we propose to extend the notion of spatial interest points into the spatio-temporal domain and show how the resulting features often reflect interesting events that can
Actions as space-time shapes
- IN ICCV
, 2005
"... Human action in video sequences can be seen as silhouettes of a moving torso and protruding limbs undergoing articulated motion. We regard human actions as three-dimensional shapes induced by the silhouettes in the space-time volume. We adopt a recent approach [14] for analyzing 2D shapes and genera ..."
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Cited by 651 (4 self)
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and generalize it to deal with volumetric space-time action shapes. Our method utilizes properties of the solution to the Poisson equation to extract space-time features such as local space-time saliency, action dynamics, shape structure and orientation. We show that these features are useful for action
Learning realistic human actions from movies
- IN: CVPR.
, 2008
"... The aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. This challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribut ..."
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Cited by 738 (48 self)
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next turn to the problem of action classification in video. We present a new method for video classification that builds upon and extends several recent ideas including local space-time features, space-time pyramids and multichannel non-linear SVMs. The method is shown to improve state
Object Recognition from Local Scale-Invariant Features
"... An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in ..."
Abstract
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Cited by 2739 (13 self)
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in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients
Evaluation of local spatio-temporal features for action recognition
- University of Central Florida, U.S.A
, 2009
"... Local space-time features have recently become a popular video representation for action recognition. Several methods for feature localization and description have been proposed in the literature and promising recognition results were demonstrated for a number of action classes. The comparison of ex ..."
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Cited by 274 (25 self)
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Local space-time features have recently become a popular video representation for action recognition. Several methods for feature localization and description have been proposed in the literature and promising recognition results were demonstrated for a number of action classes. The comparison
Local features and kernels for classification of texture and object categories: a comprehensive study
- International Journal of Computer Vision
, 2007
"... Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations an ..."
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Cited by 653 (34 self)
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Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations
Supervised and unsupervised discretization of continuous features
- in A. Prieditis & S. Russell, eds, Machine Learning: Proceedings of the Twelfth International Conference
, 1995
"... Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization, identify de n-ing characteristics of the methods, and conduct an empirical evaluation of several methods. We compare binning, an unsupervised dis ..."
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Cited by 540 (11 self)
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Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization, identify de n-ing characteristics of the methods, and conduct an empirical evaluation of several methods. We compare binning, an unsupervised
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