Dinh-Tan Pham
Hanoi University of Mining and Geology
14 Papers
24 Citations
Dinh-Tan Pham is an academic researcher from Hanoi University of Mining and Geology. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 3, co-authored 7 publications. Previous affiliations of Dinh-Tan Pham include Hanoi University of Science and Technology & Huazhong University of Science and Technology.
Chat about Author
Papers
A multi-modal multi-view dataset for human fall analysis and preliminary investigation on modality
Thanh-Hai Tran,Thi-Lan Le,Dinh-Tan Pham,Van-Nam Hoang,Van-Minh Khong,Quoc-Toan Tran,Thai-Son Nguyen,Cuong Pham +7 more
- 01 Aug 2018
TL;DR: A large continuous multimodal multivew dataset of human fall detection, namely CMDFALL, is introduced and the role of each modality is investigated to get the best results in the context of human activity recognition.
45
A robust and efficient method for skeleton-based human action recognition and its application for cross-dataset evaluation
TL;DR: TD-Net as mentioned in this paper improves the Double-Feature Double-motion Network (DD-Net) by adding a normalised coordinates of joints (NCJ) branch to enrich the spatial information.
17
Novel Skeleton-based Action Recognition Using Covariance Descriptors on Most Informative Joints
Tien-Nam Nguyen,Dinh-Tan Pham,Thi-Lan Le,Hai Vu,Thanh-Hai Tran +4 more
- 01 Nov 2018
TL;DR: A novel framework, named as Covariance Descriptor on Most Informative Joints (CovMIJ), is proposed to benefit from the simplicity of representation via covariance descriptor and noise immunity by using only most Informative joints (MIJ) for action recognition.
15
Adaptive most joint selection and covariance descriptions for a robust skeleton-based human action recognition
Van-Toi Nguyen,Tien-Nam Nguyen,Tien-Nam Nguyen,Thi-Lan Le,Dinh-Tan Pham,Dinh-Tan Pham,Hai Vu +6 more
TL;DR: The proposed method takes advantage of the skeleton data thanks to their robustness to human appearance change as well as the real-time performance, and proposes two schemes to select the most informative joints in terms of 3-D skeleton-based activity representation.
9
An Efficient Feature Fusion of Graph Convolutional Networks and Its Application for Real-Time Traffic Control Gestures Recognition
TL;DR: In this paper, the relative joints of skeleton sequences adapted in a Graph Convolutional Network (GCN) framework are combined to form the input of Attentionenhanced Adaptive GCN (AAGCN).
9