Anomaly detection in video streams is a challenging problem because of the scarcity of abnormal events and the difficulty of accurately annotating them. To alleviate these issues, unsupervised learning-based prediction methods have been previously applied. These approaches train the model with only normal events and predict a future frame from a sequence of preceding frames by use of encoder-decoder architectures so that they result in small prediction errors on normal events but large errors on abnormal events. The architecture, however, comes with the computational burden as some anomaly detection tasks require low computational cost without sacrificing performance. In this paper, Cross-Parallel Network (CPNet) for efficient anomaly detection is proposed here to minimize computations without performance drops. It consists of N smaller parallel U-Net, each of which is designed to handle a single input frame, to make the calculations significantly more efficient. Additionally, an inter-network shift module is incorporated to capture temporal relationships among sequential frames to enable more accurate future predictions.The quantitative results show that our model requires less computational cost than the baseline U-Net while delivering equivalent performance in anomaly detection.
翻译:视频流中的异常探测是一个具有挑战性的问题,因为异常事件少之又少,而且很难准确说明。为了缓解这些问题,以前曾采用过未经监督的基于学习的预测方法。这些方法只对模型进行正常事件的培训,并通过使用编码解码器结构从先前框架序列中预测一个未来框架,从而在正常事件上造成小的预测错误,但在异常事件上则出现大错。但是,由于某些异常探测任务需要低计算成本,而无需牺牲性能,因此,这种结构带来了计算负担。在本文件中,为了在不出现性能下降的情况下尽量减少计算,建议使用跨帕拉勒网络(Cros-Parallel Net)来有效检测异常现象。它由小型平行的U-Net组成,每个网络都旨在处理一个单一输入框架,使计算效率更高。此外,还采用了一个网络间转换模块,以捕捉到序列框架之间的时间关系,以便能够更准确的未来预测。定量结果显示,我们的模型所需要的计算成本比基线U-Net要低,同时在异常探测中提供同等的绩效。