Hybrid architecture for human action recognition using skeleton data

In this work, we propose a deep learning architecture, incorporating a Graph Convolutional Network (GCN) backbone combined with a partitioning transformer, that achieves results comparable to the state-of-the-art methods in skeleton based multi-person, multiview human action recognition. By leveraging attention-based GCN, the model captures context-dependent intrinsic topology while enhancing discriminative information. Furthermore, utilizing transformers, we harness their ability to aggregate long-range temporal information, allowing us to learn complex actions by attending to both short-term and long-term temporal windows. This is achieved through our partitioning strategy, which efficiently captures the relationships between neighboring and distant joints, enabling a comprehensive understanding of human movement dynamics. In this work, we also introduce a Cosine-based noise as a new data augmentation strategy for joints across time. This helps our model, Hybrid-Graformer, achieve accuracy comparable to the state-of-the-art across various skeleton-based action recognition benchmarks.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Nadeem, Muhammad Salik

Advisor dc:contributor.advisor

• Qureshi, Faisal

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1901

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1901