当前位置:网站首页>基于多路径帧预测的鲁棒无监督视频异常检测(CS CV)

基于多路径帧预测的鲁棒无监督视频异常检测(CS CV)

2020-12-07 19:16:50 凌茜

视频异常检测广泛应用于安全监控等领域,具有很大的挑战性。目前的视频异常检测方法大多采用深度重建模型,但在实际应用中,由于正常和异常视频帧之间的重建误差差不够,导致其性能往往不是最优的。同时,基于帧预测的异常检测方法表现出了良好的性能。本文提出了一种设计合理的基于帧预测的无监督视频异常检测方法,该方法更符合监控视频的特点,具有较好的鲁棒性。该方法采用了基于多路径卷积的帧预测网络,能够较好地处理具有语义信息的不同尺度的对象和区域,并捕捉正常视频中的时空相关性。在训练过程中引入噪声容限损失,以减轻背景噪声对训练的干扰。我们在中大大道、上海科技园区和UCSD行人数据集进行了广泛的实验,结果表明我们提出的方法优于现有的最先进的方法。值得注意的是,我们提出的方法在中大大道数据集上获得了88.3%的框架级AUC评分。

原文标题:Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction

原文:Video anomaly detection is commonly used in many applications such as security surveillance and is very challenging. A majority of recent video anomaly detection approaches utilize deep reconstruction models, but their performance is often suboptimal because of insufficient reconstruction error differences between normal and abnormal video frames in practice. Meanwhile, frame prediction-based anomaly detection methods have shown promising performance. In this paper, we propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design which is more in line with the characteristics of surveillance videos. The proposed method is equipped with a multi-path ConvGRU-based frame prediction network that can better handle semantically informative objects and areas of different scales and capture spatial-temporal dependencies in normal videos. A noise tolerance loss is introduced during training to mitigate the interference caused by background noise. Extensive experiments have been conducted on the CUHK Avenue, ShanghaiTech Campus, and UCSD Pedestrian datasets, and the results show that our proposed method outperforms existing state-of-the-art approaches. Remarkably, our proposed method obtains the frame-level AUC score of 88.3% on the CUHK Avenue dataset.

原文作者:Xuanzhao Wang, Zhengping Che, Ke Yang, Bo Jiang, Jian Tang, Jieping Ye, Jingyu Wang, Qi Qi

原文地址:https://arxiv.org/abs/2011.02763

原创声明,本文系作者授权云+社区发表,未经许可,不得转载。

如有侵权,请联系 yunjia_community@tencent.com 删除。

版权声明
本文为[凌茜]所创,转载请带上原文链接,感谢
https://cloud.tencent.com/developer/article/1747115