Real-time statistical anomaly detection in video

EhEye是一家监控识别软件公司，专门从事公共安全和视频监控，使用分析和人工智能。监控摄像机现在被广泛使用，然而，由于需要有经验的个人持续关注大量的视频，监控系统往往是无效的。在视频帧序列中自动快速检测个体的异常行为，例如战斗**，为提高这种有效性提供了新的令人兴奋的可能性，特别是对于大型关键基础设施位置。这将提供一个系统，可以优化现有的安全摄像头基础设施的能力，并提高安全团队对实时情况的反应能力。**然而，在实施这种自动系统方面存在一些技术挑战。该项目将解决两个关键挑战，即快速预处理数据和准确检测视频流中的异常。首先，需要将每一帧视频中的高维数据降为最相关的低维信息，以便进行有效检测；这种低维信息的计算需要在视频进行时实时执行。其次，需要根据提取的低维信息**训练出一个准确的模型**来估计打斗场景的概率**并在此基础上进行总结。用于此类预测的模型或方法可以离线训练，但在线应用时需要实时运行**。

EhEye is a surveillance recognition software company specializing in public safety and video surveillance by**using analytics and artificial intelligence. Surveillance video cameras are now widely used, however,**surveillance systems are often ineffective due to the need for experienced individuals' continuous attention to a**large number of videos. Automatic and fast detection of anomalous behaviour of individuals, such as fighting,**in a sequence of video frames offers new exciting possibilities for improving this effectiveness, especially for**large, critical infrastructure locations. This will provide a system which can optimize the capabilities of**existing security camera infrastructure and increase the security team's ability to respond to situations in real**time.**There are, however, a number of technical challenges in implementing such an automatic system. This project**will address the two key challenges - namely fast preprocessing of the data and accurate detection of anomalies**in video stream. First, the high dimensional data in each video frame needs to be reduced to the most relevant**low dimensional information needed for effective detection; the computation of this low dimensional**information needs to be performed in real time as video proceeds. Second, an accurate model needs to be**trained based on the extracted low dimensional information to estimate the probability of the fighting scenes**and conclude on it. The model or method for such prediction can be trained off-line but need to run in real time**when applied online.