Abstract
Abnormal activity recognition requires detection of occurrence of anomalous events that suffer from a severe imbalance in data. In a video, normal is used to describe activities that conform to usual events while the irregular events which do not conform to the normal are referred to as abnormal. It is far more common to observe normal data than to obtain abnormal data in visual surveillance. In this paper, we propose an approach where we can obtain abnormal data by transforming normal data. This is a challenging task that is solved through a multi-stage pipeline approach. We utilize a number of techniques from unsupervised segmentation in order to synthesize new samples of data that are transformed from an existing set of normal examples. Further, this synthesis approach has useful applications as a data augmentation technique. An incrementally trained Bayesian convolutional neural network (CNN) is used to carefully select the set of abnormal samples that can be added. Finally through this synthesis approach we obtain a comparable set of abnormal samples that can be used for training the CNN for the classification of normal vs abnormal samples. We show that this method generalizes to multiple settings by evaluating it on two real world datasets and achieves improved performance over other probabilistic techniques that have been used in the past for this task.
Abstract (translated)
异常活动识别需要检测数据严重不平衡的异常事件的发生。在视频中，normal用于描述符合常规事件的活动，而不符合常规的不规则事件则称为异常。在视觉监控中，观察正常数据比获取异常数据要常见得多。本文提出了一种通过变换正态数据来获取异常数据的方法。这是一项具有挑战性的任务，通过多级管道方法解决。我们利用一些无监督分割的技术来合成从现有的一组正常例子转换的新数据样本。此外，这种综合方法作为一种数据增强技术有着很好的应用。一个逐步训练的贝叶斯卷积神经网络（CNN）被用来仔细选择一组可以添加的异常样本。最后，通过这种综合方法，我们得到了一组可比较的异常样本，可用于培训CNN对正常样本和异常样本的分类。我们证明，该方法通过在两个真实数据集上评估多个设置，并与过去用于此任务的其他概率技术相比，提高了性能。
URL
https://arxiv.org/abs/1904.06633
