ArticleSensors (Basel, Switzerland)2021
Comparison between Recurrent Networks and Temporal Convolutional Networks Approaches for Skeleton-Based Action Recognition.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- FlashLightNet: An End-to-End Deep Learning Framework for Real-Time Detection and Classification of Static and Flashing Traffic Light States.Sensors (Basel, Switzerland) · 2025Article
- Enhanced spatiotemporal skeleton modeling: integrating part-joint attention with dynamic graph convolution.Scientific reports · 2025Article
- Efficient diagnostic classification of diverse pathologies through contextual eye movement data analysis with a novel hybrid architecture.Scientific reports · 2024Article
- Application of Deep Learning and Intelligent Sensing Analysis in Smart Home.Sensors (Basel, Switzerland) · 2024Review
- Application of AI for Short-Term PV Generation Forecast.Sensors (Basel, Switzerland) · 2023Article
- Temporal Pattern Attention for Multivariate Time Series of Tennis Strokes Classification.Sensors (Basel, Switzerland) · 2023Article
- ProtInteract: A deep learning framework for predicting protein-protein interactions.Computational and structural biotechnology journal · 2023Article
- Fast Temporal Graph Convolutional Model for Skeleton-Based Action Recognition.Sensors (Basel, Switzerland) · 2022Article
- Evaluation of 1D and 2D Deep Convolutional Neural Networks for Driving Event Recognition.Sensors (Basel, Switzerland) · 2022Article
- Skeleton-Based Spatio-Temporal U-Network for 3D Human Pose Estimation in Video.Sensors (Basel, Switzerland) · 2022Article
- An Advanced Noise Reduction and Edge Enhancement Algorithm.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
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Abstract
Action recognition plays an important role in various applications such as video monitoring, automatic video indexing, crowd analysis, human-machine interaction, smart homes and personal assistive robotics. In this paper, we propose improvements to some methods for human action recognition from videos that work with data represented in the form of skeleton poses. These methods are based on the most widely used techniques for this problem-Graph Convolutional Networks (GCNs), Temporal Convolutional Networks (TCNs) and Recurrent Neural Networks (RNNs). Initially, the paper explores and compares different ways to extract the most relevant spatial and temporal characteristics for a sequence of frames describing an action. Based on this comparative analysis, we show how a TCN type unit can be extended to work even on the characteristics extracted from the spatial domain. To validate our approach, we test it against a benchmark often used for human action recognition problems and we show that our solution obtains comparable results to the state-of-the-art, but with a significant increase in the inference speed.
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