A survey on vision-based human action recognition

By Ronald Poppe. 2010

2,431 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

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In Human-Computer Interaction

Citations in the corpus

Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks (2016, background): “It facilitates a wide range of applications such as intelligent video surveillance, human-computer interaction, and video understanding (Poppe 2010; Weinland, Ronfard, and Boyerc 2011).”
Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “Several earlier surveys were also published to review methods to recognize human poses, motions, gestures, and activities [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], as well as their applications [49], [50].”
RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017, background): “Early research on human motion recognition was dominated by the analysis of still images or videos (Aggarwal and Cai, 1999; Guo and Lai, 2014; Poppe, 2010; Turaga et al., 2008; Wang et al., 2003; Zhu et al., 2016a).”
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data (2017, background): “Demands on human action recognition techniques are growing very fast and have expanded in many domains, such as visual surveillance, human-computer interaction, video indexing/retrieval, video summary, and video understanding [27, 42].”
Finding action tubes (2014, background): “We refer to [1, 30, 43] for recent surveys in the field.”
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos (2015, background): “Thorough surveys of the literature include Poppe [20] and Weinland et al. [36].”
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data (2016, neutral): “It facilitates many applications such as intelligent video surveillance, human-computer interaction, video summary and understanding (Poppe 2010; Weinland, Ronfard, and Boyerc 2011).”
Discriminative virtual views for cross-view action recognition (2012, background): “Opportunities for the use of action analysis in domains such as surveillance, video indexing/retrieval, and human-computer interaction are growing fast [16, 18, 1], but realizing this potential relies on the ability to accurately interpret human activities from a broad range of viewing directions.”
Hierarchical Deep Temporal Models for Group Activity Recognition (2016, background): “Surveys by Weinland et al. [14] and Poppe [15] explore the vast literature in activity recognition.”
A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets (2016, background): “There is a large body of work on human activity recognition in the computer vision literature [1, 21, 29, 36].”
Inferring the Why in Images (2014, background): “We refer readers to excellent surveys [27, 1] for a full review.”
Two-person interaction detection using body-pose features and multiple instance learning (2012, neutral): “[1, 26].”
Sensor-Based Activity Recognition (2012, background): “Gavrila [5] and Poppe [6] present surveys mainly on tracking human movement via 2D or 3D models and the enabled action recognition techniques.”