人群密集度下GAN的视频异常行为检测进展

Overview on Video Abnormal Behavior Detection of GAN via Human Density

视频异常检测作为计算机视觉的重要分支,是智能监控系统中一项极具挑战性的任务,通常是指自动识别视频中的异常目标、行为或事件,对保障公共安全起着至关重要的作用。生成对抗网络是一种新兴的无监督方法,不仅可以用于生成图像,且其独特的对抗性学习思想在异常检测领域也显示出良好的发展潜力。介绍了生成对抗网络的框架结构;根据场景密度以及行为发生的对象,从个体行为异常、群体异常两个方面论述了生成对抗网络在视频异常检测领域的研究现状,分别基于重构和预测的方法对个体异常行为检测和群体异常行为检测作进一步阐述;简要介绍了视频异常检测的常用数据集;最后对未来发展作出了展望。

As an important branch of computer vision,video anomaly detection is a challenging task for intelligent video surveillance systems.It is generally referred to as automatic recognition of videos that contain abnormal targets,events or behaviors,which plays a vital role in ensuring public safety.Generative adversarial network(GAN)is anemerging unsupervised method,which can not only be used to generate images,its unique adversarial learning idea also shows good development potential in the field of anomaly detection.Firstly,the framework of the GAN is introduced.Secondly,according to the density of the scene and the object on which the action is taking place,the research status of video anomaly detection based on GAN is discussed from two aspects of individual behavior anomalies,group anomalies.These two types of abnormalities are further elaborated on the basic of reconstruction and prediction methods respectively.Thirdly,the common datasets for video anomaly detection are briefly introduced,finally,the future development is prospected.