使用深度学习的骨骼视频异常检测：综述、挑战与未来方向
Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions
AI总结：
本文综述基于视频骨骼的隐私保护深度学习异常检测方法，提出按学习范式划分的算法分类体系，论证其可行性并指出开放问题与未来方向。
摘要：
现有的视频异常检测方法大多利用包含可识别面部和基于外观特征的视频。使用带有可识别面部的视频会引发隐私问题，尤其是在医院或社区环境中使用时。基于外观的特征还可能对像素级噪声敏感，使异常检测方法在建模背景变化时承受压力，并难以聚焦于前景中人类的动作。以骨骼形式描述视频中人类运动的结构信息具有隐私保护作用，并能克服基于外观特征带来的一些问题。在本文中，我们对使用从视频中提取的骨骼、具备隐私保护能力的深度学习异常检测方法进行了综述。我们基于不同的学习方法提出了一种新颖的算法分类体系。我们得出结论：用于异常检测的基于骨骼的方法，可以成为视频异常检测的一种合理的隐私保护替代方案。最后，我们确定了主要的开放性研究问题，并为解决这些问题提供了指导方针。
英文摘要：
The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground. Structural information in the form of skeletons describing the human motion in the videos is privacy-protecting and can overcome some of the problems posed by appearance-based features. In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. We present a novel taxonomy of algorithms based on the various learning approaches. We conclude that skeleton-based approaches for anomaly detection can be a plausible privacy-protecting alternative for video anomaly detection. Lastly, we identify major open research questions and provide guidelines to address them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
