Anomaly detection is a popular and vital task in various research contexts, which has been studied for several decades. To ensure the safety of people's lives and assets, video surveillance has been widely deployed in various public spaces, such as crossroads, elevators, hospitals, banks, and even in private homes. Deep learning has shown its capacity in a number of domains, ranging from acoustics, images, to natural language processing. However, it is non-trivial to devise intelligent video anomaly detection systems cause anomalies significantly differ from each other in different application scenarios. There are numerous advantages if such intelligent systems could be realised in our daily lives, such as saving human resources in a large degree, reducing financial burden on the government, and identifying the anomalous behaviours timely and accurately. Recently, many studies on extending deep learning models for solving anomaly detection problems have emerged, resulting in beneficial advances in deep video anomaly detection techniques. In this paper, we present a comprehensive review of deep learning-based methods to detect the video anomalies from a new perspective. Specifically, we summarise the opportunities and challenges of deep learning models on video anomaly detection tasks, respectively. We put forth several potential future research directions of intelligent video anomaly detection system in various application domains. Moreover, we summarise the characteristics and technical problems in current deep learning methods for video anomaly detection.
翻译:在各种研究背景下,异常探测是一项广受欢迎的重要任务,已经进行了数十年的研究。为确保人们生命和资产的安全,在各种公共场所,如十字路口、电梯、医院、银行,甚至私人住宅,广泛部署了视频监视。深层学习表明它在许多领域的能力,从声学、图像到自然语言处理等,但设计智能视频异常探测系统并非一技之长,在不同的应用情景中造成不同异常现象。如果这种智能系统能够在我们的日常生活中实现,则有许多好处,例如大量节省人力资源,减轻政府的财政负担,以及及时和准确地确定异常行为。最近,许多关于扩大深层学习模型以解决异常探测问题的研究已经出现,从而在深层视频异常探测技术方面取得了有益的进步。然而,在本文件中,我们对从新角度探测视频异常现象的深层学习方法进行了全面审查。具体地说,我们总结了在视频异常探测任务上深层学习模型的机会和挑战,例如:大规模地节省人力资源,减轻政府的财政负担,以及及时和准确地查明异常现象的行为。最近,许多关于扩大深层学习模型模型的研究模式的研究模式的研究模式,使我们在各种深层次上发现异常现象的系统学习问题。