论文标题
部分可观测时空混沌系统的无模型预测
Anomaly detection in surveillance videos using transformer based attention model
论文作者
论文摘要
监视镜头可能会捕获广泛的现实异常。这项研究表明，使用弱监督的策略避免注释培训视频中的异常段，这很耗时。在这种方法中，仅使用视频级别标签来获得帧级别的异常得分。弱监督的视频异常检测（WSVAD）在训练过程中对异常和正常情况的错误识别错误。因此，从可用视频中提取更好质量的功能很重要。有了这种动机，本文使用了质量质量质量的特征，名为Videoswin功能，然后是基于扩张的卷积和自我关注的注意力层，以捕获时间域中的长距离和短距离依赖性。这使我们对可用视频有了更好的了解。所提出的框架在现实世界数据集（即上海校园数据集）上进行了验证，该数据集比当前的最新方法相比，其竞争性能。该模型和代码可在https://github.com/kapildeshpande/anomaly-detection-inmaly-nomaly-nomaly-detection-insubleillance-videos获得
Surveillance footage can catch a wide range of realistic anomalies. This research suggests using a weakly supervised strategy to avoid annotating anomalous segments in training videos, which is time consuming. In this approach only video level labels are used to obtain frame level anomaly scores. Weakly supervised video anomaly detection (WSVAD) suffers from the wrong identification of abnormal and normal instances during the training process. Therefore it is important to extract better quality features from the available videos. WIth this motivation, the present paper uses better quality transformer-based features named Videoswin Features followed by the attention layer based on dilated convolution and self attention to capture long and short range dependencies in temporal domain. This gives us a better understanding of available videos. The proposed framework is validated on real-world dataset i.e. ShanghaiTech Campus dataset which results in competitive performance than current state-of-the-art methods. The model and the code are available at https://github.com/kapildeshpande/Anomaly-Detection-in-Surveillance-Videos
