Mining actionlet ensemble for action recognition with depth cameras

By Jiang Wang, Jiang Wang, Zicheng Liu, Ying Wu, Ying Wu, Junsong Yuan, Junsong Yuan. 2012

1,066 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Representation & Self-Supervised Learning

Citations in the corpus

NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019, neutral): “The MSR-DailyActivity dataset [14] was among the most challenging benchmarks in this ﬁeld.”
Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks (2016, neutral): “One is to design robust and discriminative features from the skeleton (and the corresponding RGBD images) for intra-frame content representation (M ¨ uller, R ¨ oder, and Clausen 2005; Wang, Liu, and Junsong 2012; Sung et al. 2012; Yang and Tian 2014; Ji, Ye, and Cheng 2014).”
3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks (2014, neutral): “carefully designed features [36, 20, 35, 30].”
Cross-View Action Modeling, Learning, and Recognition (2014, positive): “Sample frames of Multiview Action3D dataset, cross environment test data, and MSR-DailyActivity3D dataset [19].”
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks (2017, neutral): “Joint based approaches consider the human skeleton as a set of points and use various positions based features such as joint positions [20, 31] and pairwise relative joint positions [46, 51] to characterize actions.”
A New Representation of Skeleton Sequences for 3D Action Recognition (2017, positive): “The pre-trained CNN model is leveraged as a feature extractor due to the fact that the CNN features extracted by the models pre-trained with ImageNet [22] are very powerful and have been successfully applied in a number of cross-domain applications [3, 6, 21, 10].”
Deep Learning on Lie Groups for Skeleton-Based Action Recognition (2016, neutral): “Due to the development of depth sensors, 3D human activity analysis [27, 45, 23, 43, 41, 3, 42, 37, 44, 26, 35, 17] has attracted more interest than ever before.”