With the increasing need to ensure people's safety in crowded areas, the development of a systematic anomaly detection mechanism is becoming indispensable. Here are a few examples of recent malicious attacks targeting crowded areas in big cities: in 2016, a truck driver attacked and killed 84 persons walking in the promenade in Nice, France; and on 19 December, 2016, a truck was deliberately driven into the Christmas market, in Berlin, Germany, killing 12 people and injuring 56 others. These attacks demonstrate the need for efficient monitoring systems to avoid such devastating attacks. To do so, early detection and prevention abilities are vital. Detecting and localizing abnormal events in crowded scenes is important and has significant implications in video surveillance applications. Video surveillance can be challenging, as abnormal events can be unpredictable and changing, based on the context. Accurately detecting and localizing anomalies in videos is a powerful tool that can help to improve security and understand the behavior of anomalies. In this paper, we present an automated visionbased monitoring scheme specifically designed for atypical event-detection and localization in crowded areas.

中文翻译：

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使用基于深度学习的方法在拥挤区域检测恶意攻击

随着人们在拥挤区域中确保安全的日益增长的需求，开发系统的异常检测机制变得必不可少。以下是近期针对大城市拥挤区域的恶意攻击的一些例子：2016年，一名卡车司机袭击并杀死了在法国尼斯长廊上行走的84人；2016年12月19日，一辆卡车故意驶入德国柏林的圣诞市场，炸死12人，炸伤56人。这些攻击表明需要有效的监视系统来避免此类破坏性攻击。为此，早期发现和预防能力至关重要。在拥挤的场景中检测和定位异常事件很重要，并且在视频监视应用中具有重要意义。视频监控可能会充满挑战，因为异常事件可能会根据上下文而变幻莫测。准确检测和定位视频中的异常是一个功能强大的工具，可以帮助提高安全性并了解异常行为。在本文中，我们提出了一种基于视觉的自动化监视方案，该方案专为拥挤区域中的非典型事件检测和定位而设计。