From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos

Qiao, Tanqiu; Li, Ruochen; Li, Frederick W B; Shum, Hubert P H

Abstract

Video-based Human-Object Interaction (HOI) recognition explores the intricate dynamics between humans and objects, which are essential for a comprehensive understanding of human behavior and intentions. While previous work has made significant strides, effectively integrating geometric and visual features to model dynamic relationships between humans and objects in a graph framework remains a challenge. In this work, we propose a novel end-to-end category to scenery framework, CATS, starting by generating geometric features for various categories through graphs respectively, then fusing them with corresponding visual features. Subsequently, we construct a scenery interactive graph with these enhanced geometric-visual features as nodes to learn the relationships among human and object categories. This methodological advance facilitates a deeper, more structured comprehension of interactions, bridging category-specific insights with broad scenery dynamics. Our method demonstrates state-of-the-art performance on two pivotal HOI benchmarks , including the MPHOI-72 dataset for multi-person HOIs and the single-person HOI CAD-120 dataset.

Citation

Qiao, T., Li, R., Li, F. W. B., & Shum, H. P. H. (2024, December). From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos. Presented at ICPR 2024: International Conference on Pattern Recognition, Kolkata, India

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ICPR 2024: International Conference on Pattern Recognition
• Start Date: Dec 1, 2024
• End Date: Dec 5, 2024
• Acceptance Date: Jul 1, 2024
• Online Publication Date: Dec 4, 2024
• Publication Date: Dec 4, 2024
• Deposit Date: Jul 2, 2024
• Publicly Available Date: Dec 11, 2024
• Print ISSN: 0302-9743
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 262-277
• Series Title: Lecture Notes in Computer Science
• Series Number: 15315
• Series ISSN: 0302-9743
• Book Title: Pattern Recognition 27th International Conference, ICPR 2024, Kolkata, India, December 1–5, 2024, Proceedings, Part XV
• ISBN: 9783031783531
• DOI: https://doi.org/10.1007/978-3-031-78354-8_17
• Public URL: https://durham-repository.worktribe.com/output/2514413