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REAL-TIME ABNORMAL EVENT DETECTION IN CROWDED SCENES

机译：拥挤场景中的实时异常事件检测

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Detecting unusual events in crowded scenes has drawn considerable research interest lately. In this paper, an unsupervised method that relies on a spatio-temporal descriptor and a clustering technique is presented to tackle this problem. We employ space-time auto-correlation of gradients (STACOG) descriptor to extract spatio-temporal motion features from video sequence. Following that, the K-medoids clustering algorithm is used to partition the STACOG descriptors of training frames into a set of clusters. The frame abnormality is defined by distances between the center of the clusters and the motion feature extracted by STACOG. We have conducted experiments on various benchmark datasets and the results show that the proposed method obtains comparable results: 98.48% AUC for UMN, and 92.13% accuracy for PETS 2009, at the frame level. In addition, fast computation time of our method that satisfies the demand of real-time processing.
机译：在拥挤的场景中检测异常事件最近引起了相当大的研究兴趣。本文提出了一种基于时空描述符和聚类技术的无监督方法来解决该问题。我们采用时空自相关梯度（STACOG）描述符从视频序列中提取时空运动特征。随后，使用K型聚类算法将训练帧的STACOG描述符划分为一组聚类。帧异常由群集中心与STACOG提取的运动特征之间的距离定义。我们已经在各种基准数据集上进行了实验，结果表明该方法获得了可比较的结果：UMN的AUC为98.48％，PETS 2009的准确度为92.13％。此外，我们方法的快速计算时间可以满足实时处理的需求。

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