Sparse and Contractive Graph-Based Variational Encoder-Decoder with Multihead Attention for Robust Spatiotemporal Activity Recognition

With the increasing adoption of various sensors, human action recognition has gained significant attention across multiple domains, including person surveillance and human-robot interaction. However, existing data-driven approaches struggle with effectively modeling the spatiotemporal dynamics of se...

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Vydáno v:IEEE International Conference on Electro Information Technology s. 109 - 114
Hlavní autoři: Saffari, Mohsen, Singh, Yash Pratap, Khodayar, Mahdi
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 29.05.2025
Témata:
Attention mechanisms
Correlation
Data models
Feature extraction
Generative Modeling
Graph Learning
Human Action Recognition
Human activity recognition
Multihead Attention Mechanism
Representation learning
Robustness
Sensors
Spatiotemporal phenomena
Surveillance
ISSN:2154-0373
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Shrnutí:With the increasing adoption of various sensors, human action recognition has gained significant attention across multiple domains, including person surveillance and human-robot interaction. However, existing data-driven approaches struggle with effectively modeling the spatiotemporal dynamics of sensory data and suffer from limited generalization capability. To address these challenges, this paper introduces a novel graph-based deep learning framework, incorporating a Graph-Attentive Variational Sparse Contractive Peephole LSTM (GAVSC-PLSTM) model. The proposed architecture effectively captures spatiotemporal correlations among sensory data from different body parts and introduces a novel encoder-decoder generative framework to extract task-relevant deep spatiotemporal features. Extensive experiments on three widely used public datasets demonstrate that the proposed model outperforms recent baseline methods.
ISSN:2154-0373
DOI:10.1109/eIT64391.2025.11103603