Graph Based Skeleton Motion Representation and Similarity Measurement for Action Recognition

By Pei Wang, Chunfen Yuan, Weiming Hu, Bing Li, Yanning Zhang. 2016

101 citations recorded by the source corpus.

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

How researchers see this paper

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

In Artificial Intelligence

1. Jun Liu
2. Alex ChiChung Kot
3. G. Wang
4. Ling-yu Duan
5. Amir Shahroudy
6. Kamila Abdiyeva
7. Shanxin Yuan
8. Guillermo Garcia-Hernando

In Biomedical Engineering

1. G. Wang
2. Jun Liu
3. Ling-yu Duan
4. Alex ChiChung Kot
5. Amir Shahroudy
6. Jiwen Lu
7. Kamila Abdiyeva
8. Hassen Drira

In Computer Vision and Pattern Recognition

1. Jun Liu
2. G. Wang
3. Alex ChiChung Kot
4. Ling-yu Duan
5. Amir Shahroudy
6. Luc Van Gool
7. Jiwen Lu
8. Jie Zhou

Citations in the corpus

• 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.”
• Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks (2017, methodology): “[52] proposed a graph-based motion representation in conjunction with a SPGK-kernel SVM for”
• First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations (2017, background): “Popular approaches include the use of temporal state-space models [17, 70, 74, 75, 86], key-poses [66, 85], hand-crafted pose features [64, 65], and temporal recurrent models [12, 63, 87].”
• Coding Kendall's Shape Trajectories for 3D Action Recognition (2018, background): “Another work [40] modeled a human action as a set of semantic parts called motionlets obtained by tracking then segmenting the trajectory of each joint.”
• 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”
• Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition (2018, methodology): “However, the graph representation in [5] is used to model the video, while the graph in our work is adopted to capture the topology of human body.”
• Transition Forests: Learning Discriminative Temporal Transitions for Action Recognition and Detection (2016, methodology): “Recently, deep models using Recurrent Neural Networks (RNN) [9] and Long-Short Term Memory (LSTM) [25, 39] have been proposed to model temporal dependencies, but showed inferior performance than recent (offline) models that explicitly exploit static information [28, 30] or well-suited timeseries min”
• Global Context-Aware Attention LSTM Networks for 3D Action Recognition (2017, unknown): “[57] introduced a graph-based skeleton motion representation together with a SPGK-kernel SVM for 3D action recognition.”
• Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition (2018, methodology): “We follow the standard experimental settings to perform leave-one-subject-out cross-validation (Wang et al. 2016a).”