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ABNORMAL EVENT DETECTION USING SPATIO-TEMPORAL FEATURE AND NONNEGATIVE LOCALITY-CONSTRAINED LINEAR CODING

机译:使用时空特征和非负局部限制线性编码的异常事件检测

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In this paper, an approach using the spatio-temporal feature and nonnegative locality-constrained linear coding (NLLC) is proposed to detect abnormal events in videos. This approach utilizes position-based spatio-temporal descriptors as the low-level representations of a video clip. Each descriptor consists of the position information of a space-time interest point and an appearance feature vector. To obtain the high-level video representations, the nonnegative locality-constrained linear coding is adopted to encode each spatio-temporal descriptor. Then, the max pooling integrates all NLLC codes of a video clip to produce a feature vector. Finally, the support vector machine (SVM) is employed to classify the feature vector as abnormal or normal. Experimental results on two datasets have demonstrated the promising performance of the proposed approach in the detection of both global and local abnormal events.
机译:在本文中,提出了一种方法,提出了一种方法,用于检测视频中的异常事件。该方法利用基于位置的时空描述符作为视频剪辑的低级表示。每个描述符由时空兴趣点和外观特征向量的位置信息组成。为了获得高级视频表示,采用非负局部性约束线性编码来编码每个时空描述符。然后,MAX池集成集成视频剪辑的所有NLLC代码以产生特征向量。最后,采用支持向量机(SVM)将特征向量分类为异常或正常。两个数据集的实验结果表明了在检测全球和局部异常事件中的提出方法的有希望的性能。

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