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收稿日期：2024-12-29，
修回日期：2025-02-18，
录用日期：2025-02-25，
网络出版日期：2025-02-26，
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公共场所中的社交群体检测旨在分析监控视频数据，采用社交互动、时空位置关系或计算机视觉等技术手段去表达人类的社交行为特征，进而识别交互群体。挖掘人类的社交行为模式并识别正在交互的行人群组对于轨迹预测、群体异常活动识别、人机交互等研究领域意义重大，已成为计算机视觉领域的热门研究课题。尽管取得了一些进展，群体交互现象仍然缺乏正式的规则和精确的社会解释。且由于数据采集困难，社交群体检测面临着轻量化网络设计与小样本学习困难。在这篇文章中，我们全面回顾了现有的社交行为理解与群体检测工作：首先，我们依据不同的社交行为建模角度，将公共场所中的群体检测方法分为基于启发式规则与基于学习的方法两大类，其中重点介绍了基于空间、视觉内容、行为模式的方法与主流深度学习框架；其次，我们归纳总结了评价指标、数据集与检测性能；最后，我们讨论了当前研究所面临的挑战和局限性，以及该领域未来可能的研究方向。通过对主要期刊/会议论文的调查分析，结果表明图神经网络与Transformer等的融合模型仍是主要发展趋势，但最近的基于行为模式的方法报告了最优的检测性能。因此，如何挖掘人类的本质社交模式将极具发展潜力。同时还应探索此前未被关注的隐式交互模式挖掘：隐秘犯罪团体发现。
Social group detection in public spaces has become a popular research topic in computer vision and social behavior analysis in recent years. This research aims to analyze surveillance video data， utilizing technologies such as social interaction， spatiotemporal relationships， and computer vision to identify and understand human social behavior patterns， thereby detecting pedestrian groups that are actively interacting. With the advancement of urbanization， the demand for public safety and social behavior monitoring has increased significantly. Social group detection is crucial in various research fields， including trajectory prediction， group anomaly detection， human-computer interaction， and intelligent surveillance. Exploring and identifying human social behavior patterns， especially recognizing pedestrian groups in interaction， provides strong support for applications such as predicting pedestrian behavior， detecting abnormal group activities， and enhancing public safety. This， in turn， propels further developments in computer vision and artificial intelligence. Despite some progress in this field， accurately modeling group interaction phenomena remains challenging. First， social group behaviors are diverse and complex， lacking formal rules and clear social interpretations， which makes social group detection a difficult task. Secondly， most existing methods rely on high-quality annotated data， which poses significant costs and technical challenges， especially in real-world scenarios where the number and behavior patterns of social groups are constantly changing. As a result， existing datasets often fail to cover all possible scenarios. Furthermore， social group detection faces challenges related to lightweight network design and few-shot learning. Particularly in the absence of large-scale annotated data， how to effectively train models and improve detection accuracy remains a key issue in current research. This paper reviews the existing research on social behavior understanding and social group detection. It categorizes methods for social group detection in public spaces into two main categories： heuristic-rule-based methods and learning-based methods. In heuristic-rule-based methods， researchers define a series of rules to determine the interaction features of pedestrian groups， such as using distance， speed change， relative direction， and visual descriptors to define the occurrence of social behavior， or extracting high-level semantic expressions of human interaction patterns， such as social force models or social interaction fields. However， the applicability of these methods is often limited by the accuracy and complexity of the rules. Learning-based methods， on the other hand， leverage machine learning or deep learning techniques to automatically learn the patterns of social behaviors from large datasets， thereby achieving higher detection accuracy. In recent years， deep learning methods， particularly convolutional neural networks （CNNs）， recurrent neural networks （RNNs）， and graph neural networks （GNNs）， have been widely applied in social group detection and have achieved significant results. In terms of evaluation， this paper summarizes common evaluation metrics， datasets， and detection performance. Common evaluation metrics include accuracy， recall， and F1 score， which effectively reflect the performance of social group detection algorithms. For example， when using mainstream deep learning frameworks for group detection， the average precision and recall are typically above 85%， while state-of-the-art （SOTA） behavior pattern-based methods achieve F1 scores of up to 89%. This highlights the potential of behavior patterns in recognizing social behavior and detecting interacting groups. Significant performance differences exist across various datasets， such as detection accuracy reaching 87% on the Crowd-Tracking dataset and slightly lower performance （around 83%） on the ETH dataset. These differences suggest that the diversity and complexity of datasets affect detection results to some extent， and current datasets are insufficient to cover all possible social behavior patterns. Lastly， the paper discusses the challenges and limitations of social group detection and outlines future research directions. Current challenges mainly focus on how to effectively process large-scale video data， achieve accurate group detection in data-scarce environments， and address algorithms' real-time performance and computational efficiency. Furthermore， social group detection research faces challenges in integrating cross-disciplinary knowledge， such as incorporating behavioral patterns and social psychology theories to enhance detection accuracy. Future research could expand in the following areas： first， exploring implicit social patterns， particularly covert criminal group detection； second， extracting interpretable fundamental social patterns to enhance the accuracy and robustness of social group detection； third， employing unsupervised and self-supervised learning methods to address the issue of few-shot learning， thereby improving the applicability of algorithms in real-world scenarios. In conclusion， social group detection is not only a technical challenge but also an interdisciplinary research issue with vast application potential. With continuous advancements in algorithms and hardware， future social group detection technologies will play an increasingly important role in public safety， smart cities， and human-computer interaction.
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