Learning Action Recognition Model from Depth and Skeleton Videos

By Hossein Rahmani, Mohammed Bennamoun. 2017

109 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. G. Wang
3. Amir Shahroudy
4. Alex ChiChung Kot
5. Ling-yu Duan
6. Wanqing Li
7. Pichao Wang
8. Qiuhong Ke

In Biomedical Engineering

1. Jun Liu
2. G. Wang
3. Pichao Wang
4. Wanqing Li
5. Alex ChiChung Kot
6. Amir Shahroudy
7. Ling-yu Duan
8. Hossein Rahmani

In Computer Vision and Pattern Recognition

1. Jun Liu
2. G. Wang
3. Amir Shahroudy
4. Alex ChiChung Kot
5. Wanqing Li
6. Pichao Wang
7. Ling-yu Duan
8. Hossein Rahmani

Citations in the corpus

• NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019, background): “Specifically, many of them have been evaluated based on the preliminary version [47] of our dataset, or pre-trained on it for transfer learning for other tasks [43], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79].”
• Skeleton-Based Online Action Prediction Using Scale Selection Network (2019, background): “As a result, activity analysis with 3D skeleton data becomes a popular domain of research [9], [10], [11], [12], [13], [14], [15], [16], [17] thanks to its succinctness, high level representation, and robustness against variations in viewpoints, illumination, clothing textures, and”
• Video Representation Learning Using Discriminative Pooling (2018, neutral): “…Cross-View Res-TCN (Average Pooling)[20] 74.3% 83.1% Res-TCN (Rank Pooling [2]) 75.5% 83.9% STA-LSTM [44] 73.4% 81.2% ST-LSTM + Trust Gate[28] 69.2% 77.7% Body-parts learning [33] 75.2% 83.1% SVMP (Res-TCN) 78.5% 86.4% of these methods could enjoy a further boost by applying our SVMP scheme.”
• RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017, background): “Rahmani and Bennamoun (2017) proposed an endto-end learning model for action recognition from depth and skeleton data.”
• Human Action Recognition From Various Data Modalities: A Review (2020, background): “[323] proposed an end-to-end learning framework, which consists of a CNN followed by the bilinear compact pooling and fully-connected layers for action clas-”
• Recognizing Human Actions as the Evolution of Pose Estimation Maps (2018, unknown): “80% Body-part appearance + skeleton [33] 2017 75.”
• Progressive Teacher-Student Learning for Early Action Prediction (2019, background): “Some researchers found that combining the multimodality features extracted from RGB, depth and skeleton sequences can capture more useful action information and obtain a better recognition performance [45] [17] [35] [31].”
• View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition (2018, background): “It has been demonstrated that RGB-based and skeleton-based approaches for human action recognition complement each other [34].”