视频中的异常事件检测是一个具有挑战性的视力问题。由于训练期间异常数据的稀缺,Mostexisting方法将异常事件检测制定为异常检测任务。由于关于异常事件的先前信息的缺失,这些方法不具备区分正常和异常事件的能力。在这项工作中,我们将异常事件检测形式化为一对二的分类问题。我们的贡献是双重的。首先,我们介绍基于对象中心卷积自动编码器的无监督特征学习框架,以编码运动和外观信息。其次,我们提出了一种基于将训练样本聚类为正态聚类的监督分类方法。然后使用一对一的异常事件分类器将每个正常性群集与其余群体分开。为了训练分类器,其他簇充当虚拟异常。在推理过程中,如果由一对多分类器分配的最高分类分数为负,则将对象标记为异常。综合实验在四个基准上进行:Avenue,ShanghaiTech,UCSD和UMN。我们的方法在所有四个数据集上提供了出色的结果在大规模的ShanghaiTech数据集上,与现有技术方法相比,我们的方法在帧级AUC方面提供了12.1%的绝对增益[Liuet al。,CVPR 2018]。
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及时预测重症监护室(ICU)中的临床关键事件对于提高护理和存活率非常重要。大多数现有方法基于各种分类方法的应用,从生命信号中明确地提取统计特征。在这项工作中,我们建议通过使用序列到序列自动编码器来学习它们的代表性,从多变量的生理信号时间序列中消除工程手工制作特征的高成本。然后,我们建议对已学习的表示进行分析,以便对关键事件的预测进行信号相似性评估。我们将这种方法论框架应用于急性低血压事件(AHE),对大量不同的生命信号记录数据集进行了应用。实验证明了所提出的框架能够准确预测即将到来的AHE的能力。
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如今,多变量时间序列数据越来越多地收集在各​​种现实世界系统中,例如发电厂,可穿戴设备等。多变量时间序列中的异常检测和诊断是指在某些时间步骤中识别异常状态并查明根本原因。然而,这样的系统具有挑战性,因为它不仅需要捕获每个时间序列中的时间依赖性,而且还需要编码不同时间序列对之间的相关性。此外,系统应该对噪声具有鲁棒性,并根据不同事件的严重程度为操作员提供不同级别的异常分数。尽管已经开发了许多无监督的异常检测算法,但是它们中很少能够共同解决这些挑战。在本文中,我们提出了一种多尺度卷积递归编码器 - 解码器(MSCRED),用于在多变量时间序列数据中进行性能检测和诊断。具体来说,MSCRED首先构建多尺度(分辨率)签名矩阵,以在不同的时间步长中表征系统状态的多个级别。随后,给定签名矩阵,使用卷积编码器来编码传感器间(时间序列)相关性和注意力。基于卷积长短期记忆(ConvLSTM)网络被开发用于捕获时间模式。最后,基于编码传感器间相关性和时间信息的特征图,使用卷积解码器重建输入签名矩阵,并且进一步利用残余签名矩阵来检测和诊断异常。基于合成数据集和真实发电厂数据集的广泛实证研究表明,MSCRED可以胜过最先进的基线方法。
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在这项工作中,我们研究了医疗时间系列的无监督表示学习,它承诺利用大量现有的标记数据,以便最终协助临床决策。通过评估临床相关结果的预测,我们表明,在实用设置中,无监督表示学习可以提供比端到端监督体系结构更好的性能优势。我们尝试以两种不同的方式使用序列到序列(Seq2Seq)模型,作为自动编码器和预测器,并且表明通过具有集成注意机制的预测Seq2Seq模型实现了最佳性能,在设置中首次提出无监督学习的医疗时间系列。
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Due to the continued digitization of industrial and societal processes, including the deployment of networked sensors , we are witnessing a rapid proliferation of time-ordered observations, known as time series. For example, the behavior of drivers can be captured by GPS or accelerometer as a time series of speeds, directions, and accelerations. We propose a framework for outlier detection in time series that, for example, can be used for identifying dangerous driving behavior and hazardous road locations. Specifically, we first propose a method that generates statistical features to enrich the feature space of raw time series. Next, we utilize an autoencoder to reconstruct the enriched time series. The autoencoder performs dimensionality reduction to capture, using a small feature space, the most representative features of the enriched time series. As a result, the reconstructed time series only capture representative features, whereas outliers often have non-representative features. Therefore, deviations of the enriched time series from the reconstructed time series can be taken as indicators of outliers. We propose and study autoencoders based on convolutional neural networks and long-short term memory neural networks. In addition, we show that embedding of contextual information into the framework has the potential to further improve the accuracy of identifying outliers. We report on empirical studies with multiple time series data sets, which offers insight into the design properties of the proposed framework, indicating that it is effective at detecting outliers.
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我们将异常事件检测问题表示为异常检测任务,并提出了一种基于k均值聚类和一类支持向量机(SVM)的两阶段算法来消除异常值。在从仅包含正常事件的训练视频中提取运动特征之后,我们应用k均值聚类来找到表示不同类型运动的聚类。在第一阶段,我们认为具有较少样本的集群(相对于给定的阈值)仅包含异常值,并且我们完全消除这些集群。在第二阶段,我们通过在每个集群上训练一类SVM模型来缩小剩余集群的边界。为了检测测试视频中的异常事件,我们分析每个测试样本并考虑由训练的一类SVM模型提供的最大正态性分数,基于测试样本只能属于一个正常运动集群的直觉。如果测试样品不适合任何变窄的簇,则标记为异常。我们还将基于运动特征的方法与基于使用预先训练的卷积神经网络(CNN)提取的深度外观特征的近期方法相结合。我们使用后期融合策略将我们的两阶段算法与深度框架相结合,使两个方法的管道保持独立。我们将我们的方法与四个基准数据集上的几种最先进的监督和无监督方法进行比较。实证结果表明,在大多数情况下,我们的异常事件检测框架可以获得更好的结果,同时在CPU上以每秒32帧的速度实时处理测试视频。
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Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.
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循环自动编码器模型通过编码器结构将顺序数据汇总成固定长度的矢量,然后通过解码器结构重建原始序列。汇总向量可用于表示时间序列特征。在本文中,我们建议放宽解码器输出的维度,以便执行部分重构。因此,固定长度矢量仅表示所选尺寸的特征。此外,我们建议使用滚动固定窗口方法从无界时间序列数据生成训练样本。随着时间的推移,时间序列特征的变化可以概括为平滑的轨迹路径。使用附加可视化和无监督聚类技术进一步分析固定长度矢量。所提出的方法可以应用于用于传感器信号分析的大规模工业过程,其中矢量表示的集群可以反映工业系统的操作状态。
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In the study of various diseases, heterogeneity among patients usually leads to diierent progression paaerns and may require diierent types of therapeutic intervention. erefore, it is important to study patient subtyping, which is grouping of patients into disease characterizing subtypes. Subtyping from complex patient data is challenging because of the information heterogeneity and temporal dynamics. Long-Short Term Memory (LSTM) has been successfully used in many domains for processing sequential data, and recently applied for analyzing longitudinal patient records. e LSTM units are designed to handle data with constant elapsed times between consecutive elements of a sequence. Given that time lapse between successive elements in patient records can vary from days to months, the design of traditional LSTM may lead to suboptimal performance. In this paper, we propose a novel LSTM unit called Time-Aware LSTM (T-LSTM) to handle irregular time intervals in longitudinal patient records. We learn a subspace decomposition of the cell memory which enables time decay to discount the memory content according to the elapsed time. We propose a patient subtyp-ing model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes. Experiments on synthetic and real world datasets show that the proposed T-LSTM architecture captures the underlying structures in the sequences with time irregularities.
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Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine. However, there are often external factors or variables which are not captured by sensors leading to time-series which are inherently unpredictable. For instance, manual controls and/or unmonitored environmental conditions or load may lead to inherently unpredictable time-series. Detecting anomalies in such scenarios becomes challenging using standard approaches based on mathematical models that rely on stationarity, or prediction models that utilize prediction errors to detect anomalies. We propose a Long Short Term Memory Networks based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD) that learns to reconstruct 'normal' time-series behavior, and thereafter uses reconstruction error to detect anomalies. We experiment with three publicly available quasi predictable time-series datasets: power demand , space shuttle, and ECG, and two real-world engine datasets with both predictive and unpredictable behavior. We show that EncDec-AD is robust and can detect anomalies from predictable , unpredictable, periodic, aperiodic, and quasi-periodic time-series. Further, we show that EncDec-AD is able to detect anomalies from short time-series (length as small as 30) as well as long time-series (length as large as 500).
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We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes. While most existing works merely use hand-crafted appearance and motion features, we propose Appearance and Motion DeepNet (AMDN) which utilizes deep neural networks to automatically learn feature representations. To exploit the complementary information of both appearance and motion patterns, we introduce a novel double fusion framework, combining both the benefits of traditional early fusion and late fusion strategies. Specifically, stacked denoising autoencoders are proposed to separately learn both appearance and motion features as well as a joint representation (early fusion). Based on the learned representations, multiple one-class SVM models are used to predict the anomaly scores of each input, which are then integrated with a late fusion strategy for final anomaly detection. We evaluate the proposed method on two publicly available video surveillance datasets, showing competitive performance with respect to state of the art approaches.
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异常事件检测是视频监控研究和实际应用的重要目标之一。然而,在实际环境中对于大多数异常检测系统仍存在三个具有挑战性的问题:有限的标记数据,“异常”的模糊定义和昂贵的特征工程步骤。本文介绍了一个统一的检测框架,使用基于能量的模型来处理这些挑战,这些模型是非监督表示学习的强大工具。我们提出的模型首先通过输入视频而不是手工制作的视觉特征来训练图像帧的未标记原始像素;然后根据输入视频与模型产生的重建之间的误差识别出异常对象的位置。为了处理视频流,我们开发了一个在线版本的框架,其中模型参数以动态到达的图像帧递增地更新。我们的实验表明,我们的探测器使用受限玻尔兹曼机器(RBM)和深玻尔兹曼机器(DBM)作为核心模块,可以在无监督的基线上实现卓越的异常检测性能,并且在像素评估时获得与最先进方法相当的精度-水平。更重要的是,我们发现用DBM训练的我们的系统能够同时执行场景聚类和场景重建。这种能力不仅将我们的方法与其他现有的探测器区分开来,而且还提供了一种独特的工具来研究和理解模型的工作原理。
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Detection of atrial fibrillation (AF), a type of cardiac arrhythmia, is difficult since many cases of AF are usually clinically silent and undiagnosed. In particular paroxysmal AF is a form of AF that occurs occasionally, and has a higher probability of being undetected. In this work, we present an attention based deep learning framework for detection of paroxysmal AF episodes from a sequence of windows. Time-frequency representation of 30 seconds recording windows, over a 10 minute data segment, are fed sequentially into a deep convolutional neural network for image-based feature extraction , which are then presented to a bidirectional recurrent neural network with an attention layer for AF detection. To demonstrate the effectiveness of the proposed framework for transient AF detection , we use a database of 24 hour Holter Electrocardiogram (ECG) recordings acquired from 2850 patients at the University of Virginia heart station. The algorithm achieves an AUC of 0.94 on the testing set, which exceeds the performance of baseline models. We also demonstrate the cross-domain generalizablity of the approach by adapting the learned model parameters from one recording modality (ECG) to another (photoplethysmogram) with improved AF detection performance. The proposed high accuracy, low false alarm algorithm for detecting paroxysmal AF has potential applications in long-term monitoring using wearable sensors.
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由于数据量大,因此增加了对自主和通用异常检测系统的需求。然而,开发一种准确且快速的独立的通用异常检测系统仍然是一个挑战。在本文中,我们提出了传统的时间序列分析方法,季节自回归整合移动平均(SARIMA)模型和使用黄土(STL)的SeasonalTrend分解,以检测复杂和各种异常。通常,SARIMA和STL仅用于静止和周期时间 - 系列,但通过组合,我们表明他们可以检测高精度的异常,甚至嘈杂和非周期性的数据。我们将该算法与Long ShortTerm Memory(LSTM)进行了比较,LSTM是一种用于异常检测系统的基于深度学习的算法。我们总共使用了七个真实数据集和四个具有不同时间序列属性的人工数据集来验证所提算法的性能。
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我们提出了一种半监督模型,用于检测由视频像素网络引发的视频异常[van den Oord et al。,2016]。 VPN是基于深度神经网络的概率生成模型,其估计视频帧中原始像素的离散联合分布。我们的模型扩展了VPN的卷积LSTM视频编码器部分,带来了一种新颖的基于卷积的关注机制。我们还将VPN的Pixel-CNN解码器部分修改为帧修复任务,其中帧topredict的部分掩码版本作为输入给出。帧重建错误用作异常指示符。我们在移动的mnist数据集的修改版本上测试我们的模型[Srivastava et al。,2015]。我们的模型被证明可以有效地检测视频中的恶意。这种方法可以成为需要视觉常识的应用程序的一个组成部分。
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Anomaly detection in database management systems (DBMSs) is difficult because of increasing number of statistics (stat) and event metrics in big data system. In this paper, I propose an automatic DBMS diagnosis system that detects anomaly periods with abnormal DB stat metrics and finds causal events in the periods. Reconstruction error from deep autoencoder and statistical process control approach are applied to detect time period with anomalies. Related events are found using time series similarity measures between events and abnormal stat metrics. After training deep autoencoder with DBMS metric data, efficacy of anomaly detection is investigated from other DBMSs containing anomalies. Experiment results show effectiveness of proposed model, especially, batch temporal normalization layer. Proposed model is used for publishing automatic DBMS diagnosis reports in order to determine DBMS configuration and SQL tuning.
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Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection across millions of metrics. Classical time series models are often used in conjunction with a probabilistic formulation for uncertainty estimation. However, such models are hard to tune, scale, and add exogenous variables to. Motivated by the recent resurgence of Long Short Term Memory networks, we propose a novel end-to-end Bayesian deep model that provides time series prediction along with uncertainty estimation. We provide detailed experiments of the proposed solution on completed trips data, and successfully apply it to large-scale time series anomaly detection at Uber.
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In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely Deep Learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.
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无监督视频摘要每天在消化,浏览和搜索不断增长的视频中起着重要作用,并且几乎没有触及在线视频中的基本的细粒度语义和运动信息(即,感兴趣的对象和他们的关键运动)。本文研究了细粒度无监督对象级视频摘要的开拓性研究方向。它可以在两个方面区别于现有管道:提取参与对象的关键动作,以及以无人监督和在线方式进行总结的学习。为了实现这一目标,我们提出了一种新的在线运动自动编码器(在线运动AE)框架,该框架在超分段物体运动剪辑上起作用。对新收集的监视数据集和公共数据集的综合实验证明了我们提出的方法的有效性。 。
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With the growing popularity of short-form video sharing platforms such as Instagram and Vine, there has been an increasing need for techniques that automatically extract highlights from video. Whereas prior works have approached this problem with heuristic rules or supervised learning, we present an unsupervised learning approach that takes advantage of the abundance of user-edited videos on social media websites such as YouTube. Based on the idea that the most significant sub-events within a video class are commonly present among edited videos while less interesting ones appear less frequently, we identify the significant sub-events via a robust recurrent auto-encoder trained on a collection of user-edited videos queried for each particular class of interest. The auto-encoder is trained using a proposed shrinking exponential loss function that makes it robust to noise in the web-crawled training data, and is configured with bidirectional long short term memory (LSTM) [5] cells to better model the temporal structure of highlight segments. Different from supervised techniques, our method can infer highlights using only a set of down-loaded edited videos, without also needing their pre-edited counterparts which are rarely available online. Extensive experiments indicate the promise of our proposed solution in this challenging unsupervised setting.
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