论文标题
通过暹罗网络学习距离功能，以在视频中定位异常
Learning a distance function with a Siamese network to localize anomalies in videos
论文作者
论文摘要
这项工作介绍了一种新方法来在监视视频中定位异常。主要的新颖性是使用暹罗卷积神经网络（CNN）学习一对视频贴片（视频时空区域）之间的距离函数的想法。学到的距离功能不是针对目标视频的特定特定的，用于测量测试视频中每个视频补丁与正常培训视频中发现的视频补丁之间的距离。如果测试视频补丁与任何普通视频补丁不同，则必须是异常的。我们使用4种评估措施和3种挑战性目标基准数据集比较了先前发表的算法的方法。实验表明，我们的方法要么超过或执行与当前的最新方法相当。
This work introduces a new approach to localize anomalies in surveillance video. The main novelty is the idea of using a Siamese convolutional neural network (CNN) to learn a distance function between a pair of video patches (spatio-temporal regions of video). The learned distance function, which is not specific to the target video, is used to measure the distance between each video patch in the testing video and the video patches found in normal training video. If a testing video patch is not similar to any normal video patch then it must be anomalous. We compare our approach to previously published algorithms using 4 evaluation measures and 3 challenging target benchmark datasets. Experiments show that our approach either surpasses or performs comparably to current state-of-the-art methods.
