基于LDA模型的CCTV系统压缩域实时异常事件检测

A. Diop
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引用次数: 1

摘要

为了存储和显示,大量的数据是由闭路电视系统产生的。这些系统的压缩域异常事件的自动检测使得提取这些事件以发出警报成为可能,并可能以压缩格式存储序列以优化数据的存储和传输容量。本文介绍了一种实时异常事件检测的解决方案。提出了一种基于LDA模型的CCTV系统压缩域事件分类方法。实验结果表明,使用LDA模型对事件进行分类,可以在两个标准化数据集上以非常高的压缩率提取压缩域中的异常事件,准确率达到95%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-Time Abnormal Event Detection in the Compressed Domain of CCTV Systems by LDA Model
For storage and displaying, huge amounts of data are produced by the CCTV (Close Circuit TeleVision) systems. The automatic detection of abnormal events in the compressed domain of these systems makes it possible to extract these events to alert and possibly store the sequences in a compressed format to optimize the capacity of storage and transfer of data. This paper describes a solution for a real-time abnormal event detection. The proposed method is based on the LDA model for classifying events in the compressed domain in CCTV systems. Experimental results, demonstrating reliable real-time extractions and storage, shows that the classification of events with the LDA model allows the extraction of abnormal events in the compressed domain at very high compression rate with an accuracy of 95% for two standardized datasets considered.
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