Anomaly detection in videos has been attracting an increasing amount of attention. Despite the competitive performance of recent methods on benchmark datasets, they typically lack desirable features such as modularity, cross-domain adaptivity, interpretability, and real-time anomalous event detection. Furthermore, current state-of-the-art approaches are evaluated using the standard instance-based detection metric by considering video frames as independent instances, which is not ideal for video anomaly detection. Motivated by these research gaps, we propose a modular and unified approach to the online video anomaly detection and localization problem, called MOVAD, which consists of a novel transfer learning based plug-and-play architecture, a sequential anomaly detector, a mathematical framework for selecting the detection threshold, and a suitable performance metric for real-time anomalous event detection in videos. Extensive performance evaluations on benchmark datasets show that the proposed framework significantly outperforms the current state-of-the-art approaches.
Nanjing University of Science and Technology（南京理工大学）
Video anomaly detection under video-level labels is currently a challenging task. Previous works have made progresses on discriminating whether a video sequencecontains anomalies. However, most of them fail to accurately localize the anomalous events within videos in the temporal domain. In this paper, we propose a Weakly Supervised Anomaly Localization (WSAL) method focusing on temporally localizing anomalous segments within anomalous videos. Inspired by the appearance difference in anomalous videos, the evolution of adjacent temporal segments is evaluated for the localization of anomalous se…
VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs
Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Edinburgh（爱丁堡大学）；Khalifa University（哈利法大学）；Linköping University（林雪平大学）；Australian National University（澳大利亚国立大学）
Topo-R1: Detecting Topological Anomalies via Vision-Language Models
Stony Brook University（石溪大学）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；Stanford University（斯坦福大学）；Penn State University（宾夕法尼亚州立大学）
Proactive network maintenance (PNM) is the concept of using data from a network to identify and locate network faults, many or all of which could worsen to become service failures. The separation between the network fault and the service failure affords early detection of problems in the network to allow PNM to take place. Consequently, PNM is a form of prognostics and health management (PHM). The problem of localizing and classifying anomalies on 1-dimensional data series has been under research for years. We introduce a new algorithm that leverages Deep Convolutional Neural Networks to effic…
This brief sketches initial progress towards a unified energy-based solution for the semi-supervised visual anomaly detection and localization problem. In this setup, we have access to only anomaly-free training data and want to detect and identify anomalies of an arbitrary nature on test data. We employ the density estimates from the energy-based model (EBM) as normalcy scores that can be used to discriminate normal images from anomalous ones. Further, we back-propagate the gradients of the energy score with respect to the image in order to generate a gradient map that provides pixel-level sp…
University of Science Technology of China（中国科学技术大学）；Meka Technology Co Ltd（美卡科技有限公司）
We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anomalies. SimpleNet consists of four components: (1) a pre-trained Feature Extractor that generates local features, (2) a shallow Feature Adapter that transfers local features towards target domain, (3) a simple Anomaly Feature Generator that counterfeits anomaly features by adding Gaussian noise to normal features, and (4) a binary Anomaly Discriminator that distinguishes anomaly features from normal features. During inference, the Anomaly Feature Generator would be discarded. Our approach is…
EventVAD: Training-Free Event-Aware Video Anomaly Detection
Peking University（北京大学）；Guangdong University of Technology（广东工业大学）；The University of Sheffield（谢菲尔德大学）；University of Science and Technology Beijing（北京科技大学）；Tsinghua University（清华大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Nanjing University（南京大学）；University of Trento（特伦特大学）；Queen's University Belfast（贝尔法斯特女王大学）
Self-supervised learning for classifying paranasal anomalies in the maxillary sinus
University Medical Center Hamburg-Eppendorf（汉堡埃彭多夫大学医学中心）；Department of Otorhinolaryngology, Head and Neck Surgery and Oncology（耳鼻喉科、头颈外科及肿瘤科）；Clinic and Polyclinic for Diagnostic and Interventional Radiology and Nuclear Medicine（诊断与介入放射学及核医学临床与门诊）；Population Health Research Department, University Heart and Vascular Center（大学心脏血管中心人群健康研究部）；Clinic and Polyclinic for Neurology（神经科临床与门诊）
Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China（计算机功率网络与信息安全重点实验室，教育部，山东计算机科学中心（济南国家超级计算机中心），齐鲁大学（山东科学院），济南，中国）；Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, China（山东省计算功率互联网与服务计算重点实验室，山东省计算机科学基础研究中心，济南，中国）
University of Illinois at Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校）；Pohang University of Science and Technology (POSTECH)（浦项科技大学）
Most recent studies on detecting and localizing temporal anomalies have mainly employed deep neural networks to learn the normal patterns of temporal data in an unsupervised manner. Unlike them, the goal of our work is to fully utilize instance-level (or weak) anomaly labels, which only indicate whether any anomalous events occurred or not in each instance of temporal data. In this paper, we present WETAS, a novel framework that effectively identifies anomalous temporal segments (i.e., consecutive time points) in an input instance. WETAS learns discriminative features from the instance-level l…
Action Hints: Semantic Typicality and Context Uniqueness for Generalizable Skeleton-based Video Anomaly Detection
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家重点实验室）；National Engineering Research Center for Visual Information（视觉信息工程国家级工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究所）；Robotics, Xi'an Jiaotong University（机器人学，西安交通大学）；School of Electronics and Control Engineering（电子控制工程学院）
VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer
State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology, Anhui University, Hefei, China（光电信息采集与防护技术国家重点实验室，安徽大学，合肥，中国）；School of Artificial Intelligence, Anhui University, Hefei, China（人工智能学院，安徽大学，合肥，中国）；College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China（智能科学与技术学院，国防科技大学，长沙，中国）
University of Central Florida（中佛罗里达大学）；Mitsubishi Electric Research Laboratories（三菱电机研究实验室）；Siemens Corporate Technology（西门子企业技术）
Anomaly localization is an important problem in computer vision which involves localizing anomalous regions within images with applications in industrial inspection, surveillance, and medical imaging. This task is challenging due to the small sample size and pixel coverage of the anomaly in real-world scenarios. Most prior works need to use anomalous training images to compute a class-specific threshold to localize anomalies. Without the need of anomalous training images, we propose Convolutional Adversarial Variational autoencoder with Guided Attention (CAVGA), which localizes the anomaly wit…
OoDDINO:A Multi-level Framework for Anomaly Segmentation on Complex Road Scenes
College of Computer Science and Artificial Intelligence, Southwest Minzu University（计算机科学与人工智能学院，西南民族大学）；Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, China（可持续城市智能交通工程研究中心，教育部，中国）
Reconstruction-Based Anomaly Localization via Knowledge-Informed Self-Training
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；Ningbo Research Institute, Zhejiang University（浙江大学宁波研究院）
Indian Institute of Information Technology, Nagpur（那格浦尔印度信息技术学院）；Visvesvaraya National Institute of Technology, Nagpur（维斯瓦拉亚国家理工学院那格浦尔分校）
Intelligent resident surveillance is one of the most essential smart community services. The increasing demand for security needs surveillance systems to be able to detect anomalies in surveillance scenes. Employing high-capacity computational devices for intelligent surveillance in residential societies is costly and not feasible. Therefore, we propose anomaly detection for intelligent surveillance using CPU-only edge devices. A modular framework to capture object-level inferences and tracking is developed. To cope with partial occlusions, posture deformations, and complex scenes, we employed…
Traffic anomaly detection has played a crucial role in Intelligent Transportation System (ITS). The main challenges of this task lie in the highly diversified anomaly scenes and variational lighting conditions. Although much work has managed to identify the anomaly in homogenous weather and scene, few resolved to cope with complex ones. In this paper, we proposed a dual-modality modularized methodology for the robust detection of abnormal vehicles. We introduced an integrated anomaly detection framework comprising the following modules: background modeling, vehicle tracking with detection, mas…
