In recent years, the spread of video sensor networks both in public and private areas has grown considerably. Smart algorithms for video semantic content understanding are increasingly developed to support human operators in monitoring different activities, by recognizing events that occur in the observed scene. With the term event, we refer to one or more actions performed by one or more subjects (e.g., people or vehicles) acting within the same observed area. When these actions are performed by subjects that do not interact with each other, the events are usually classified as simple. Instead, when any kind of interaction occurs among subjects, the involved events are typically classified as complex. This survey starts by providing the formal definitions of both scene and event, and the logical architecture for a generic event recognition system. Subsequently, it presents two taxonomies based on features and machine learning algorithms, respectively, which are used to describe the different approaches for the recognition of events within a video sequence. This paper also discusses key works of the current state-of-the-art of event recognition, providing the list of datasets used to evaluate the performance of reported methods for video content understanding.

中文翻译：

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机器学习用于视频事件识别

近年来，视频传感器网络在公共和私有区域中的普及已大大增加。通过识别在观察到的场景中发生的事件，越来越多地开发了用于视频语义内容理解的智能算法，以支持操作员监视不同的活动。对于事件一词，我们指的是在同一观察区域内行动的一个或多个对象（例如，人或车辆）执行的一项或多项动作。当这些动作由彼此不交互的对象执行时，事件通常被分类为简单事件。相反，当主体之间发生任何类型的交互时，所涉及的事件通常被归类为复杂事件。这项调查首先提供场景和事件的正式定义以及通用事件识别系统的逻辑体系结构。随后，它分别提出了两个基于特征和机器学习算法的分类法，用于描述视频序列中事件识别的不同方法。本文还讨论了当前事件识别技术的关键工作，提供了用于评估所报告方法对视频内容理解方法的性能的数据集列表。