SportsHHI: A Dataset for Detecting Complex Human-Human Interactions in Sports Videos

核心概念

The core message of this article is to propose a new video visual relation detection task focused on understanding complex human-human interactions in multi-person sports videos, and to introduce the SportsHHI dataset to benchmark this task.

摘要

The article proposes a new video visual relation detection task called "video human-human interaction detection", which aims to detect and recognize high-level interactions between humans in complex multi-person sports videos. The authors develop a new dataset named SportsHHI to support this task.

Key highlights:

• Current video visual relation detection datasets have limitations in exploring complex human-human interactions in multi-person scenarios, and the relation types defined have relatively low-level semantics.
• SportsHHI is built on basketball and volleyball sports videos, containing 34 high-level interaction classes such as technical actions, tactical cooperation, and confrontation.
• SportsHHI provides 118,075 human bounding boxes and 50,649 interaction instances annotated on 11,398 keyframes, which is comparable in scale to existing video scene graph generation datasets.
• The authors propose a two-stage baseline method for the human-human interaction detection task and conduct extensive experiments to reveal key factors for a successful interaction detector, such as motion features, context information, relative position encoding, and information exchange among proposals.
• The authors hope SportsHHI can stimulate research on human interaction understanding in videos and promote the development of spatio-temporal context modeling techniques in video visual relation detection.

統計資料

SportsHHI contains 34 high-level interaction classes from basketball and volleyball sports.
118,075 human bounding boxes and 50,649 interaction instances are annotated on 11,398 keyframes.
The dataset is split into 38,527 training instances from 8,719 keyframes and 12,122 validation instances from 2,679 keyframes.

引述

"SportsHHI contains 34 high-level interaction classes from basketball and volleyball sports."
"118,075 human bounding boxes and 50,649 interaction instances are annotated on 11,398 keyframes."

從以下內容提煉的關鍵洞見

SportsHHI

by Tao Wu,Runyu... 於 arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04565.pdf

深入探究

How can the SportsHHI dataset be extended to other team sports beyond basketball and volleyball to further broaden the understanding of human-human interactions in complex multi-person scenarios

To extend the SportsHHI dataset to other team sports beyond basketball and volleyball, several steps can be taken. Firstly, a thorough analysis of the dynamics and interactions specific to each sport should be conducted to identify key interaction classes. This analysis can involve consulting experts in the respective sports to understand the nuances of human-human interactions. Once the interaction classes are defined, videos from different team sports can be annotated with these interactions, similar to the process followed for basketball and volleyball in the SportsHHI dataset. The dataset can include sports such as soccer, American football, rugby, or hockey, each with its unique set of interactions. By expanding the dataset to cover a variety of team sports, researchers can gain a more comprehensive understanding of human-human interactions in complex multi-person scenarios across different athletic activities.

What are the potential challenges in applying the proposed human-human interaction detection methods to real-world applications beyond sports, such as surveillance or social interaction analysis

Applying the proposed human-human interaction detection methods from sports to real-world applications beyond sports, such as surveillance or social interaction analysis, may present several challenges. One significant challenge is the diversity and complexity of interactions in real-world scenarios. Unlike sports videos where interactions are often structured and rule-based, interactions in surveillance or social settings can be more spontaneous and varied. This variability can make it challenging to define a comprehensive set of interaction classes and accurately detect them in diverse contexts. Additionally, the presence of occlusions, varying lighting conditions, and background clutter in real-world environments can impact the performance of detection models trained on sports-specific datasets. Adapting the models to handle these real-world challenges while maintaining high accuracy and robustness is crucial for successful deployment in practical applications.

How can the insights gained from modeling high-level human-human interactions in sports videos be leveraged to improve the understanding of more general human social behaviors and group dynamics

Insights gained from modeling high-level human-human interactions in sports videos can be leveraged to improve the understanding of more general human social behaviors and group dynamics in several ways. Firstly, the techniques developed for detecting interactions in sports videos, such as spatio-temporal context modeling and multi-person scene analysis, can be applied to social interaction analysis to capture complex social dynamics. By incorporating high-level semantic information and detailed spatio-temporal reasoning, models can better interpret social interactions in various settings. Furthermore, the dataset annotations and interaction definitions from sports videos can serve as a valuable reference for creating datasets focused on social interactions, enabling researchers to explore a wider range of human behaviors. By transferring knowledge and methodologies from sports to social contexts, researchers can enhance the understanding of human interactions across different domains.