ID 原文 译文
39906 视频异常事件检测与定位旨在检测视频中发生的异常事件，并锁定其在视频中发生的位置。 Video anomaly event detection and localization aim to detect abnormal events and lock its localization in the video.
39907 但是视频场景复杂多样，并且异常发生的位置随机多变，导致发生的异常事件难以被精准定位。 However, the video scenes are complex and diverse, and the localizations where anomaly events occur are random and changeable, which makes it difficult to accurately locate the occurred abnormal events.
39908 本文提出了一种基于卷积自编码器分块学习的视频异常事件检测与定位方法， This paper proposes a video anomaly event detection and localization method based on convolutional auto-encoder patch learning.
39909 首先将视频帧进行均匀划分，提取视频帧中每一块的光流和方向梯度直方图（Histogram of oriented gradient， HOG）特征， Firstly, we divide the video frames evenly into patches and extract the optical flow and the histogram of oriented gradient （HOG） feature of each patch.
39910 然后为视频中的不同图块分别设计卷积自编码器以学习正常运动模式特征， Then, at the different patches in the video, we individually design a convolutional auto-encoder to learn the feature in the normal motion mode.
39911 最后在异常事件检测过程中利用卷积自编码器的重构误差大小进行异常判断。 During the anomaly event detection process, the reconstruction loss of the convolutional auto-encoder is used for anomaly detection.
39912 该方法可以有效地针对视频不同区域进行特征学习，提升了异常事件定位的准确度。 The proposed method can effectively perform feature learning for different regions of the video and improve the accuracy of anomaly event localization.
39913 所提方法在UCSD Ped1、UCSD Ped2、CUHK Avenue三个公开数据集上进行实验，结果表明该方法能够准确定位异常事件，并且帧级别AUC（Area under the curve）平均提升了5.61%。 Experimental results on three public datasets, UCSD Ped1, UCSD Ped2, and CUHK Avenue, demonstrate that the frame level AUC （area under the curve） of this method is increased by 5.61% on average and can accurately locate anomaly events.
39914 心率变异性（Heart rate variability， HRV）被广泛用于临床自主神经系统评估和心率异常分类，传统的HRV分析基于心电图（Electrocardiogram， ECG）、光容积图（Photoplethysmography， PPG）和远程光容积图（Remote PPG， RPPG）， Heart rate variability （HRV） is widely used in clinical autonomic nervous system assessment and classification of abnormal heart rate. Traditional HRV analysis is based on electrocardiogram （ECG）, photoplethysmography （PPG） and remote PPG （RPPG）.
39915 这些方法存在诸多不足：（1）ECG检测需在皮肤涂抹刺激性的耦合剂并附加电极，不宜长期监测且ECG设备价格昂贵； However, these methods have the following disadvantages： （1） The detection of ECG requires the application of irritating coupling agent on the skin and additional electrodes, which is not suitable for long-term monitoring, and the ECG equipment is expensive；