A Hough transform-based voting framework for action recognition

By Angela Yao, Juergen Gall, Luc Van Gool. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010

301 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d935bf80ac85cc7a749fb76f8ddb68406fe9d25a

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In Biomedical Engineering

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Citations in the corpus

A Review of Human Activity Recognition Methods (2015, background): “Random forests for action representation have also attracted widespread interest for action recognition Mikolajczyk and Uemura (2008) and Yao et al. (2010).”
Coupled Action Recognition and Pose Estimation from Multiple Views (2012, methodology): “We use the same low-level appearance features as (Yao et al. 2010): colour, dense optical flow (Brox et al.”
Poselet Key-Framing: A Model for Human Activity Recognition (2013, unknown): “[37] – 88% Vahdat et al.”
A Hierarchical Representation for Future Action Prediction (2014, unknown): “[26] 88% Vahdat et al.”
Random Forests for Real Time 3D Face Analysis (2012, background): “…in semantic segmentation (Shotton et al. 2008), keypoint recognition (Lepetit et al. 2005), object detection (Gall and Lempitsky 2009; Gall et al. 2011), action recognition (Yao et al. 2010; Gall et al. 2011), and real time human pose estimation (Shotton et al. 2011; Girshick et al. 2011).”
Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification (2014, methodology): “2013), COPAR, JDL and the methods in (Qiu et al. 2011; Yao et al. 2010; Sadanand and Corso 2012).”
Hough Forests for Object Detection, Tracking, and Action Recognition (2011, background): “The resulting single detector is not only more convenient, but also achieves higher accuracy in multi-aspect detection tasks.”
Temporal Localization of Actions with Actoms (2013, unknown): “[4, 6, 7, 10–12 , 38– 45”
Learning Sparse Representations for Human Action Recognition (2012, methodology): “Apparantly, our recognition rate is lower than that reported in [15], [20], [35] and [36]; but notice that unlike [15], [20] we have not enlarged2”
End-to-End Learning of Action Detection from Frame Glimpses in Videos (2015, background): “It must learn to effectively utilize these observations, or frame glimpses, to reason on the temporal bounds of actions.”
Learning Tags from Unsegmented Videos of Multiple Human Actions (2011, background): “pre-segmented or strongly labeled data for training [30], [18], [29], [7].”
SST: Single-Stream Temporal Action Proposals (2017, unknown): “[4, 12, 16, 34, 37, 39].”
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation (2015, background): “The idea of probabilistic voting was also exploited in object tracking [18], action recognition [28], facial feature detection [12], and human pose estimation [13].”
Video Action Detection with Relational Dynamic-Poselets (2014, unknown): “Action detection has been comprehensively studied [8,14,34,4,5,32,9,33,20,19].”