Recognizing human actions using multiple features

By Jingen Liu, Saad Ali, Mubarak Shah. 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008

321 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a1cba8beccdaf04b53a4b5c8bf4469071d20ac6e

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Learning deformable action templates from cluttered videos (2009, background): “This poses a challenge for many approaches on action recognition [1, 5, 23, 11] that assume the foreground segmentation (silhouette) is available.”
A survey of vision-based methods for action representation, segmentation and recognition (2011, unknown): “local features and silhouette based features [65].”
3D Flow Estimation for Human Action Recognition from Colored Point Clouds (2013, methodology): “…body (Yilmaz & Shah, 2005; Blank et al., 2005; Rusu et al., 2009), 2D optical flow (Efros et al., 2003; Yacoob & Black, 1998; Ali & Shah, 2010) and 3D spatio-temporal volumes (Yilmaz & Shah, 2005; Blank et al., 2005; Rusu et al., 2009; Ke et al., 2005; Liu et al., 2008a; Scovanner et al., 2007).”
Understanding Video Events: A Survey of Methods for Automatic Interpretation of Semantic Occurrences in Video (2009, background): “Recently, the investigation of this domain has gained popularity partly because of the availability of public databases[40, 41], but largely because of the need to compare and contrast different abstraction schemes and their effect on event recognition performance [61, 62, 63, 64, 65].”
Human Activity Detection from RGBD Images (2011, methodology): “Several authors have supplemented these techniques by adding more information to these features (Jhuang et al., 2007; Wong, Kim, and Cipolla, 2007; Wu et al., 2007; Liu, Ali, and Shah, 2008).”
Cross-view action recognition via view knowledge transfer (2011, background): “Some leading representations include space-time pattern templates [3, 27], shape features [36, 24, 7, 19], interest point based representations [15, 26, 29, 20], learned geometrical models of the human body parts [12], and motion/optical flow patterns [1, 25, 36].”
Cross-View Action Recognition via Transferable Dictionary Learning (2016, background): “Higher-level descriptors introduced in [26], [28], and [36] exploited correlations between local descriptors for selection.”
Slow Feature Analysis for Human Action Recognition (2012, methodology): “In this paper, we introduce the SFA framework to the problem of human action recognition by incorporating the discriminative information with SFA learning and considering the spatial relationship of body parts.”
A Spatio-Temporal Descriptor Based on 3D-Gradients (2008, unknown): “Liu et al. [12] obtain the best accuracy known to us (90.4”
Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning (2010, unknown): “Note that work by [3] and [4] can also be categorized under a volume-based representation.”
Unstructured human activity detection from RGBD images (2011, methodology): “We have also made the dataset and code available open-source at: http://pr.cs.cornell.edu/humanactivities”
Fast Exact Hyper-graph Matching with Dynamic Programming for Spatio-temporal Data (2014, unknown): “[34] have used graphs to model the relationship between different components.”