Surveillance videos are able to capture a variety of realistic anomalies. In this paper, we propose to learn anomalies by exploiting both normal and anomalous videos. To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i.e. the training labels (anomalous or normal) are at videolevel instead of clip-level. In our approach, we consider normal and anomalous videos as bags and video segments as instances in multiple instance learning (MIL), and automatically learn a deep anomaly ranking model that predicts high anomaly scores for anomalous video segments. Furthermore, we introduce sparsity and temporal smoothness constraints in the ranking loss function to better localize anomaly during training.We also introduce a new large-scale first of its kind dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery, etc. as well as normal activities. This dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities. Our experimental results show that our MIL method for anomaly detection achieves significant improvement on anomaly detection performance as compared to the state-of-the-art approaches. We provide the results of several recent deep learning baselines on anomalous activity recognition. The low recognition performance of these baselines reveals that our dataset is very challenging and opens more opportunities for future work. The dataset is

Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Recently, two-stage self-training methods have achieved significant improvements by self-generating pseudo labels and self-refining anomaly scores with these labels. As the pseudo labels play a crucial role, we propose an enhancement framework by exploiting completeness and uncertainty properties for effective self-training. Specifically, we first design a multi-head classification module (each head serves as a classifier) with a diversity loss to maximize the distribution differences of predicted pseudo labels across heads. This encourages the generated pseudo labels to cover as many abnormal events as possible. We then devise an iterative uncertainty pseudo label refinement strategy, which improves not only the initial pseudo labels but also the updated ones obtained by the desired classifier in the second stage. Extensive experimental results demonstrate the proposed method performs favorably against state-of-the-art approaches on the UCF-Crime, TAD, and XD-Violence benchmark datasets.

开放式视频异常检测（OpenVAD）旨在从视频数据中识别出异常事件，在测试中都存在已知的异常和新颖的事件。无监督的模型仅从普通视频中学到的模型适用于任何测试异常，但遭受高误报率的损失。相比之下，弱监督的方法可有效检测已知的异常情况，但在开放世界中可能会失败。我们通过将证据深度学习（EDL）和将流量（NFS）归一化为多个实例学习（MIL）框架来开发出一种新颖的OpenVAD问题的弱监督方法。具体而言，我们建议使用图形神经网络和三重态损失来学习训练EDL分类器的区分特征，在该特征中，EDL能够通过量化不确定性来识别未知异常。此外，我们制定了一种不确定性感知的选择策略，以获取清洁异常实例和NFS模块以生成伪异常。我们的方法通过继承无监督的NF和弱监督的MIL框架的优势来优于现有方法。多个现实世界视频数据集的实验结果显示了我们方法的有效性。

这项工作解决了弱监督的异常检测，其中允许预测指标不仅可以从正常示例中学习，而且还可以从训练期间提供的一些标签异常。特别是，我们处理视频流中异常活动的本地化：这是一个非常具有挑战性的情况，因为培训示例仅带有视频级别的注释（而不是帧级）。最近的几项工作提出了各种正则化术语来解决它，即通过对弱学习的框架级异常得分的稀疏性和平滑度约束。在这项工作中，我们受到自我监督学习领域的最新进展的启发，并要求模型为同一视频序列的不同增强而产生相同的分数。我们表明，执行这种对齐能够提高模型在XD暴力方面的性能。

在当代社会中，监视异常检测，即在监视视频中发现异常事件，例如犯罪或事故，是一项关键任务。由于异常发生很少发生，大多数培训数据包括没有标记的视频，没有异常事件，这使得任务具有挑战性。大多数现有方法使用自动编码器（AE）学习重建普通视频；然后，他们根据未能重建异常场景的出现来检测异常。但是，由于异常是通过外观和运动来区分的，因此许多先前的方法使用预训练的光流模型明确分开了外观和运动信息，例如。这种明确的分离限制了两种类型的信息之间的相互表示功能。相比之下，我们提出了一个隐式的两路AE（ITAE），其中两个编码器隐含模型外观和运动特征以及一个将它们组合在一起以学习正常视频模式的结构。对于正常场景的复杂分布，我们建议通过归一化流量（NF）的生成模型对ITAE特征的正常密度估计，以学习可拖动的可能性，并使用无法分布的检测来识别异常。 NF模型通过隐式学习的功能通过学习正常性来增强ITAE性能。最后，我们在六个基准测试中演示了ITAE及其特征分布建模的有效性，包括在现实世界中包含各种异常的数据库。

视频中的战斗检测是当今监视系统和流媒体的流行率的新兴深度学习应用程序。以前的工作主要依靠行动识别技术来解决这个问题。在本文中，我们提出了一种简单但有效的方法，该方法从新的角度解决了任务：我们将战斗检测模型设计为动作感知功能提取器和异常得分生成器的组成。另外，考虑到视频收集帧级标签太费力了，我们设计了一个弱监督的两阶段训练计划，在此我们使用在视频级别标签上计算出的多个实体学习损失来培训得分生成器，并采用自我训练的技术以进一步提高其性能。在公开可用的大规模数据集（UBI-Fights）上进行了广泛的实验，证明了我们方法的有效性，并且数据集的性能超过了几种先前的最先进的方法。此外，我们收集了一个新的数据集VFD-2000，该数据集专门研究视频战斗检测，比现有数据集更大，场景更大。我们的方法的实现和拟议的数据集将在https://github.com/hepta-col/videofightdetection上公开获得。

我们提出了一种轻巧，准确的方法，用于检测视频中的异常情况。现有方法使用多个实体学习（MIL）来确定视频每个段的正常/异常状态。最近的成功研​​究认为，学习细分市场之间的时间关系很重要，以达到高精度，而不是只关注单个细分市场。因此，我们分析了近年来成功的现有方法，并发现同时学习所有细分市场确实很重要，但其中的时间顺序与实现高准确性无关。基于这一发现，我们不使用MIL框架，而是提出具有自发机制的轻质模型，以自动提取对于确定所有输入段正常/异常非常重要的特征。结果，我们的神经网络模型具有现有方法的参数数量的1.3％。我们在三个基准数据集（UCF-Crime，Shanghaitech和XD-Violence）上评估了方法的帧级检测准确性，并证明我们的方法可以比最新方法实现可比或更好的准确性。

Video anomaly detection (VAD) -- commonly formulated as a multiple-instance learning problem in a weakly-supervised manner due to its labor-intensive nature -- is a challenging problem in video surveillance where the frames of anomaly need to be localized in an untrimmed video. In this paper, we first propose to utilize the ViT-encoded visual features from CLIP, in contrast with the conventional C3D or I3D features in the domain, to efficiently extract discriminative representations in the novel technique. We then model long- and short-range temporal dependencies and nominate the snippets of interest by leveraging our proposed Temporal Self-Attention (TSA). The ablation study conducted on each component confirms its effectiveness in the problem, and the extensive experiments show that our proposed CLIP-TSA outperforms the existing state-of-the-art (SOTA) methods by a large margin on two commonly-used benchmark datasets in the VAD problem (UCF-Crime and ShanghaiTech Campus). The source code will be made publicly available upon acceptance.

We develop a novel framework for single-scene video anomaly localization that allows for human-understandable reasons for the decisions the system makes. We first learn general representations of objects and their motions (using deep networks) and then use these representations to build a high-level, location-dependent model of any particular scene. This model can be used to detect anomalies in new videos of the same scene. Importantly, our approach is explainable - our high-level appearance and motion features can provide human-understandable reasons for why any part of a video is classified as normal or anomalous. We conduct experiments on standard video anomaly detection datasets (Street Scene, CUHK Avenue, ShanghaiTech and UCSD Ped1, Ped2) and show significant improvements over the previous state-of-the-art.

单个异常行为因人群的大小，上下文和场景而异。当检测，跟踪和认可异常行为的人时，诸如部分阻塞，模糊，大数字异常行为和摄像机观看之类的挑战发生在大规模的人群中。在本文中，我们的贡献是双重的。首先，我们介绍了一个注释和标记的大规模人群异常行为hajj数据集（hajjv2）。其次，我们提出了两种混合卷积神经网络（CNN）和随机森林（RFS）的两种方法，以检测和识别小型和大型人群视频中的时空异常行为。在小型人群视频中，对Resnet-50预训练的CNN模型进行了微调，以验证空间域中的每个帧是正常还是异常。如果观察到异常行为，则使用基于运动的个体检测方法基于角链光流的大小和方向来定位和跟踪具有异常行为的个体。大规模人群视频中使用了Kalman过滤器，以预测和跟踪随后的帧中检测到的个体。然后，将均值，方差和标准偏差统计特征计算出来并馈送到RF，以对时间域中的行为异常行为进行分类。在大规模的人群中，我们使用Yolov2对象检测技术微调Resnet-50模型，以检测空间域中行为异常的个体。

视频异常检测最近在弱监督下作为多个实例学习任务制定，其中每个视频都被视为要确定是否包含异常的片段。先前的努力主要集中于摘要本身的歧视，而无需对时间动力进行建模，这是指相邻摘要的变化。因此，我们提出了一种具有两个目标函数的歧视动力学学习（DDL）方法，即动态排名损耗和动态对齐损失。前者的目标是扩大正面和负袋之间的分数动态差距，而后者则在袋中进行特征动力学和得分动力学的时间对齐。此外，构建了一个局部意识的注意网络（LA-NET），以捕获全局相关性并重新校准跨段的位置偏好，然后是带有因果卷积的多层感知器以获得异常得分。实验结果表明，我们的方法在两个具有挑战性的基准（即UCF-Crime和XD-Violence）上取得了重大改进。

In recent years, we have seen a significant interest in data-driven deep learning approaches for video anomaly detection, where an algorithm must determine if specific frames of a video contain abnormal behaviors. However, video anomaly detection is particularly context-specific, and the availability of representative datasets heavily limits real-world accuracy. Additionally, the metrics currently reported by most state-of-the-art methods often do not reflect how well the model will perform in real-world scenarios. In this article, we present the Charlotte Anomaly Dataset (CHAD). CHAD is a high-resolution, multi-camera anomaly dataset in a commercial parking lot setting. In addition to frame-level anomaly labels, CHAD is the first anomaly dataset to include bounding box, identity, and pose annotations for each actor. This is especially beneficial for skeleton-based anomaly detection, which is useful for its lower computational demand in real-world settings. CHAD is also the first anomaly dataset to contain multiple views of the same scene. With four camera views and over 1.15 million frames, CHAD is the largest fully annotated anomaly detection dataset including person annotations, collected from continuous video streams from stationary cameras for smart video surveillance applications. To demonstrate the efficacy of CHAD for training and evaluation, we benchmark two state-of-the-art skeleton-based anomaly detection algorithms on CHAD and provide comprehensive analysis, including both quantitative results and qualitative examination.

Object movement identification is one of the most researched problems in the field of computer vision. In this task, we try to classify a pixel as foreground or background. Even though numerous traditional machine learning and deep learning methods already exist for this problem, the two major issues with most of them are the need for large amounts of ground truth data and their inferior performance on unseen videos. Since every pixel of every frame has to be labeled, acquiring large amounts of data for these techniques gets rather expensive. Recently, Zhao et al. [1] proposed one of a kind Arithmetic Distribution Neural Network (ADNN) for universal background subtraction which utilizes probability information from the histogram of temporal pixels and achieves promising results. Building onto this work, we developed an intelligent video surveillance system that uses ADNN architecture for motion detection, trims the video with parts only containing motion, and performs anomaly detection on the trimmed video.

检测视频中的异常事件通常被帧为单级分类任务，其中培训视频仅包含正常事件，而测试视频包含正常和异常事件。在这种情况下，异常检测是一个开放式问题。然而，一些研究吸收异常检测行动识别。这是一个封闭式场景，无法测试检测到新的异常类型时系统的能力。为此，我们提出UbnorMal，这是一个由多个虚拟场景组成的新的监督开放式基准，用于视频异常检测。与现有数据集不同，我们首次引入在训练时间的像素级别注释的异常事件，从而实现了用于异常事件检测的完全监督的学习方法。为了保留典型的开放式配方，我们确保在我们的培训和测试集合中包括Disjoint集的异常类型。据我们所知，Ubnormal是第一个视频异常检测基准，以允许一流的开放模型和监督闭合模型之间的公平头部比较，如我们的实验所示。此外，我们提供了实证证据，表明Ubnormal可以提高两个突出数据集，大道和上海学习的最先进的异常检测框架的性能。

视频异常检测是视觉中的核心问题。正确检测和识别视频数据中行人中的异常行为将使安全至关重要的应用，例如监视，活动监测和人类机器人的互动。在本文中，我们建议利用无监督的行人异常事件检测的轨迹定位和预测。与以前的基于重建的方法不同，我们提出的框架依赖于正常和异常行人轨迹的预测误差来在空间和时间上检测异常。我们介绍了有关不同时间尺度的现实基准数据集的实验结果，并表明我们提出的基于轨迹预言的异常检测管道在识别视频中行人的异常活动方面有效有效。代码将在https://github.com/akanuasiegbu/leveraging-trajectory-prediction-for-pedestrian-video-anomaly-detection上提供。

对于弱监督的异常检测，由于无法对长期上下文信息进行建模，大多数现有工作仅限于视频表示不足的问题。为了解决这个问题，我们提出了一个新型弱监督的自适应图卷积网络（WAGCN），以模拟视频片段之间复杂的上下文关系。通过此，我们完全考虑了其他视频片段对当前段的影响，在为每个段的异常概率分数生成当前段。首先，我们结合了视频片段的时间一致性以及功能相似性来构建全局图，该图可以充分利用视频中异常事件的时空特征之间的关联信息。其次，我们提出了一个图形学习层，以打破手动设置拓扑的限制，该拓扑可以根据数据自适应地提取图形邻接矩阵。在两个公共数据集（即UCF-Crime数据集和Shanghaitech数据集）上进行了广泛的实验，证明了我们的方法的有效性，从而实现了最先进的性能。

Weakly supervised video anomaly detection (WSVAD) is a challenging task since only video-level labels are available for training. In previous studies, the discriminative power of the learned features is not strong enough, and the data imbalance resulting from the mini-batch training strategy is ignored. To address these two issues, we propose a novel WSVAD method based on cross-batch clustering guidance. To enhance the discriminative power of features, we propose a batch clustering based loss to encourage a clustering branch to generate distinct normal and abnormal clusters based on a batch of data. Meanwhile, we design a cross-batch learning strategy by introducing clustering results from previous mini-batches to reduce the impact of data imbalance. In addition, we propose to generate more accurate segment-level anomaly scores based on batch clustering guidance further improving the performance of WSVAD. Extensive experiments on two public datasets demonstrate the effectiveness of our approach.

The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground. Structural information in the form of skeletons describing the human motion in the videos is privacy-protecting and can overcome some of the problems posed by appearance-based features. In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. We present a novel taxonomy of algorithms based on the various learning approaches. We conclude that skeleton-based approaches for anomaly detection can be a plausible privacy-protecting alternative for video anomaly detection. Lastly, we identify major open research questions and provide guidelines to address them.

这项工作的目的是检测并自动生成视频中异常事件的高级解释。了解异常事件的原因至关重要，因为所需的响应取决于其性质和严重程度。最近的作品通常使用对象或操作分类器来检测和提供异常事件的标签。然而，这将检测系统限制为有限的已知类别，并防止到未知物体或行为的概括。在这里，我们展示了如何在不使用对象或操作分类器的情况下稳健地检测异组织，但仍然恢复事件背后的高级原因。我们提出以下贡献：（1）一种使用显着性图来解除对象和动作分类器的异常事件解释的方法，（2）显示如何使用新的神经架构来学习视频的离散表示来提高显着图的质量通过预测未来帧和（3）将最先进的异常解释方法击败60 \％在公共基准X-MAN数据集的子集上。

无人驾驶飞机（UAV）通过低成本，大型覆盖，实时和高分辨率数据采集能力而广泛应用于检查，搜索和救援行动的目的。在这些过程中产生了大量航空视频，在这些过程中，正常事件通常占压倒性的比例。本地化和提取异常事件非常困难，这些事件包含手动从长视频流中的潜在有价值的信息。因此，我们致力于开发用于解决此问题的异常检测方法。在本文中，我们创建了一个新的数据集，名为Droneanomaly，用于空中视频中的异常检测。该数据集提供了37个培训视频序列和22个测试视频序列，这些视频序列来自7个不同的现实场景，其中包括各种异常事件。有87,488个彩色视频框架（训练51,635，测试35,853），每秒30帧的尺寸为640美元\ times 640美元。基于此数据集，我们评估现有方法并为此任务提供基准。此外，我们提出了一种新的基线模型，即变压器（ANDT）的异常检测，该模型将连续的视频帧视为一系列小管，它利用变压器编码器从序列中学习特征表示，并利用解码器来预测下一帧。我们的网络模型在训练阶段模型正常，并确定了具有不可预测的时间动力学的事件，作为测试阶段的异常。此外，为了全面评估我们提出的方法的性能，我们不仅使用无人机 - 异常数据集，而且使用另一个数据集。我们将使我们的数据集和代码公开可用。可以在https://youtu.be/ancczyryoby上获得演示视频。我们使数据集和代码公开可用。