SkeletonNet: Mining Deep Part Features for 3-D Action Recognition

By Qiuhong Ke, Senjian An, Mohammed Bennamoun, Ferdous Sohel, Farid Boussaïd. 2017

158 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, 3D Vision & Pose

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Citations in the corpus

NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019, background): “Wang et al. [81] proposed a “scene ﬂow to action map” representation for RGB+D based action recognition with CNNs. Ke et al. [82] transformed the 3D skeleton data to ten feature arrays and input them to CNNs for action recognition.”
Skeleton-Based Online Action Prediction Using Scale Selection Network (2019, neutral): “research domain [26], [27], and a series of hand-crafted features [28], [29], [30], [31], [32], [33], [34], [35], [36], [37] and deep learning-based approaches [18], [19], [38], [39], [40], [41], [42], [43], [44] have been proposed.”
A New Representation of Skeleton Sequences for 3D Action Recognition (2017, background): “With the prevalence of highlyaccurate and affordable devices, action recognition based on 3D skeleton sequence has been attracting increasing attention [49, 42, 6, 37, 54, 26, 22, 46, 19].”
Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “features learned from skeleton data are not always translation, scale, and rotation invariant, several methods [180], [181] have also been designed to handle these issues.”
A Real-Time and Hardware-Efficient Processor for Skeleton-Based Action Recognition With Lightweight Convolutional Neural Network (2019, methodology): “In this brief, we use normalized magnitude (NM) and cosine distance (CD) as the features for action classification, which are proposed by [10] and invariant to the selection of coordinate, translation and rotation.”
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN (2018, unknown): “rTable 4. Results of all skeleton based methods on NTU RGB+D dataset. Method CS CV Deep learning on Lie Group [15] 61.37% 66.95% JTM+CNN [49] 73.40% 75.20% Res-TCN [22] 74.30% 83.10% SkeletonNet(CNN) [20] 75.94% 81.16% JDM+CNN [31] 76.20% 82.30% Clips+CNN+MTLN [21] 79.57% 84.83% Enhanced Visualizati”
SSNet: Scale Selection Network for Online 3D Action Prediction (2018, methodology): “After the development of cheap and easy-to-use depth sensors, such as Kinect and XTion, human action recognition in 3D skeleton sequences becomes very popular [1, 79], and a series of hand-crafted features [72, 63, 75, 64, 52, 13, 70, 67] and deep learning based approaches [11, 83, 51, 30, 42, 26] h”
Leveraging Structural Context Models and Ranking Score Fusion for Human Interaction Prediction (2016, background): “CNN pre-trained models have been shown to be transferable across domains [29], and have achieved a better performance than hand-crafted features in a variety of visual recognition tasks [30], [31], [32], [33], [34].”
Joint Human Detection and Head Pose Estimation via Multistream Networks for RGB-D Videos (2017, background): “tion [24], [25], action recognition [26]–[28], and pose estimation [29]–[31].”