Exploring the space of a human action

By Yaser Sheikh, Mumtaz Sheikh, Mubarak Shah. 2005

264 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “[174] Action Space Conc Lowlv Hand X X X X Yilma and Shah [175] Multiview Geometry Conc Lowlv Hand X X Gong et al.”
Kernelized covariance for action recognition (2016, background): “, subspace view invariant metric [3]) or applied stochastic techniques to model the degree of uncertainty in the data.”
Machine Recognition of Human Activities: A Survey (2008, background): “[133] have identified three important sources that give rise to variability in observed features.”
The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition (2009, background): “Commonly used parameters are joint angles or joint locations of articulated human models [16, 12].”
Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data (2016, background): “Joint-based approaches consider human skeleton as a set of points, and represent it using features like joint positions [5, 8, 9, 13, 16, 21, 26], joint orientations in a fixed coordinate system [24, 37, 32, 22], pairwise relative joint positions [34, 35, 36, 39], etc.”
View-invariant action recognition using fundamental ratios (2008, background): “affine [12], or projective [11], rank constraints on the action space represented by a set of basis functions [13], or the use of epipolar geometry induced by the same pose in different views [14, 17, 6].”
Human activity analysis (2011, methodology): “They not only have tested their system on the existing KTH dataset and the Weizmann dataset [Blank et al. 2005], but also on their own dataset constructed by gathering clips from movie scenes.”
Inverse Dynamics for Action Recognition (2013, background): “[9] expressed an action as a linear combination of spatiotemporal actions and proposed a framework for learning the variability of the execution of human actions that is unaffected by the changes.”
A survey on vision-based human action recognition (2010, unknown): “[5,104,116,131,132,174]).”
A survey on activity recognition and behavior understanding in video surveillance (2013, unknown): “Sheikh [171]”
View-Invariant Action Recognition from Point Triplets (2009, methodology): “An intriguing question that may arise is then the following: If this is true for any two scene planes, then why does the similarity measure based on the eigenvalue constraint proposed above work?”
A survey of vision-based methods for action representation, segmentation and recognition (2011, methodology): “[199℄ A. Yilmaz, M. Shah, A tions sket h: A novel a tion representation, in:IEEE Conferen e on Computer Vision and Pattern Re ognition, 2005, pp.”
Exploring discriminative pose sub-patterns for effective action classification (2013, background): “Similar methodologies can be found in [26, 35, 28, 3], where either trajectories formed by joints [28, 35, 3] or curvature patterns of trajectories formed from body parts [26] are treated as features.”
Event Detection in Crowded Videos (2007, background): “These can be broadly categorized into approaches based on tracking [21, 26], flow [8,14,24], spatio-temporal shapes [2,3,31,32], and interest points [7, 20, 23].”
Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group (2014, methodology): “D XYZT space was used in [15] to represent a human action, and their affine projections were compared using a subspace angles-based view-invariant similarity measure.”
Recognising action as clouds of space-time interest points (2009, background): “Existing human action recognition methods can be broadly classified into four categories: flow based [4], spatio-temporal shape template based [7, 8, 21], interest points based [3, 18, 11, 17, 10, 6, 22], and tracking based [16, 1, 15, 19].”