Human–Object Interaction Detection: An Overview

doi:10.1109/MCE.2023.3343919

Human–Object Interaction Detection: An Overview

2024-11-01

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DOI：10.1109/MCE.2023.3343919

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Abstract

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This article systematically summarizes and discusses recent research on image-based human-object interaction (HOI) detection, which aims to detect human-object pairs and recognize the interactive behaviors between humans and objects in an image. It has plenty of applications and can serve as the basis to assist higher level tasks of visual understanding. We introduce existing methods by categorizing them into two main groups based on the model structure: one-stage and two-stage approaches. We further divide one-stage methods into point-based, region-based, and query-based methods. Similarly, the two-stage methods are divided into HOI detection with multistream modeling, HOI detection with human parts and pose, HOI detection with compositional learning, HOI detection with graph-based modeling, and HOI detection with query-based modeling. According to this taxonomy, we also summarize and analyze the core ideas behind each strategy. Then, we present the details of the experimental protocols, evaluation metrics, datasets, and the evaluation results of the most recent representative methods. Finally, we discuss the main open challenges and future trends in the HOI detection task.

Keywords:

Feature extraction

Task analysis

Visualization

Cognition

Affordances

Convolutional neural networks

Consumer electronics

Human–Object Interaction Detection: An Overview

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