Papers Video Anomaly Detection
“Video Anomaly Detection” 태그가 달린 논문 344편 · 필터 해제
Adaptive Multi-Granularity Temporal Modeling for Weakly Supervised Video Anomaly Detection
As the scale of video surveillance data outpaces manual annotation capacities, weakly supervised video anomaly detection (WSVAD) has emerged as a critical research frontier. Most existing approaches formulate WSVAD withi…
Multiple Instance LearningVideo Anomaly DetectionEvent SegmentationA VLM Answer Is Not an Anomaly Score: Rank Compression Across Image and Video Anomaly Detection
Anomaly detection aims to identify observations that deviate from normal patterns. Recent work uses pretrained vision-language models (VLMs) for training-free image and video anomaly detection without task-specific retra…
Video Anomaly DetectionSTEP: Score-Based Temporal Energy for Human Pose Video Anomaly Detection
Skeleton-based Video Anomaly Detection (VAD) offers a robust, privacy-preserving solution for identifying abnormal behaviors. To model the distribution of normal static and moving poses, recent methods train Energy-Based…
Computational EfficiencyVideo Anomaly DetectionPose EstimationRethinking Open-World Video Anomaly Detection: Diagnosing Definition Blindness
Open-world video anomaly detection (OWVAD) is expected to detect events that match a user-specified definition of abnormality. This requirement is stronger than generic anomaly localization: in the same video, changing t…
Video Anomaly DetectionO-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning
Industrial Video Anomaly Detection (IVAD) aims to identify anomalous objects and events in an industrial process, which is crucial for modern manufacturing and quality control systems. Existing VLM-based anomaly reasonin…
Video Anomaly DetectionStructured Evidence Selection for Weakly Supervised Video Anomaly Detection
Weakly supervised video anomaly detection relies solely on video-level labels for training, making it difficult to accurately localize anomalous events in complex scenes. In real-world videos, anomalous behaviors exhibit…
Computational EfficiencyVideo Anomaly DetectionEvent Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithms
Video anomaly detection (VAD) is critical for automated surveillance but remains fragile under challenging conditions such as illumination variations, fast motion, and complex backgrounds when relying solely on visible l…
Video Anomaly DetectionLatent Clarity: Bridging World-Model Kinematics to Semantic Manifolds for Video Anomaly Anticipation
Continuous video anomaly detection is dominated by reactive Multiple Instance Learning (MIL) that collapses spatiotemporal features into scalar scores. We introduce PULS (Predictive Unified Latent Space), a continuous se…
Multiple Instance LearningVideo Anomaly DetectionLinguistic Relative Policy Optimization for Video Anomaly Reasoning
Video anomaly detection (VAD) with multimodal large language models has shown strong potential, yet most existing methods still depend on large-scale annotations or expert-designed priors, limiting their ability to acqui…
Video Anomaly DetectionSENSE-VAD: Sentient and Semantic Video Anomaly Detection for Autonomous Driving
Autonomous vehicles (AVs) must navigate not only motion-based hazards but also socially complex situations whose danger is constituted by inter-agent relationships rather than movement statistics alone. A child running a…
Video Anomaly DetectionAutonomous VehiclesAutonomous DrivingLearning Where and When: Patch-Based Spatiotemporal Localization in Weakly Supervised Video Anomaly Detection
Weakly supervised video anomaly detection (WSVAD) has predominantly focused on temporal localization, identifying when anomalies occur while largely neglecting their spatial extent within frames. Yet, spatial localizatio…
Multiple Instance LearningVideo Anomaly DetectionBenchmark AUC Is Not Deployable Reliability: A Cross-Dataset Audit of Off-the-Shelf Features for Surveillance Video Anomaly Detection
Automated "suspicious behavior" flagging is a headline promise of AI surveillance, and the field reports high frame-level ROC-AUC on standard video anomaly detection benchmarks. Those numbers are measured by training and…
Video Anomaly DetectionReliability-Aware Prototype Calibration for Frozen Pose-Flow Video Anomaly Detection
Pose-flow video anomaly detectors are attractive for one-class surveillance because they provide likelihood-based rankings for tracked skeleton windows. However, a single likelihood score may hide multimodal normal behav…
Video Anomaly DetectionMemoVAD: Resource-Efficient Video Anomaly Detection via Dynamic Semantic Memory in Edge Computing Scenarios
Deploying Video Anomaly Detection (VAD) in real-world surveillance faces a fundamental tension between the demand for high-level semantics to ensure effectiveness and the limited computational resources of edge devices. …
Video Anomaly DetectionVigilFormer: Deformable Attention for Video Anomaly Detection with Causal Risk Inference
Video anomaly detection in surveillance settings must balance detection accuracy against real-time throughput, a tension that existing methods address either through stronger feature extractors or more efficient architec…
Video Anomaly DetectionAn Analysis Focused on Womens Safety: Can VAD Models Be Enhanced by a Multi-modal Dataset?
Women's safety and security are paramount for a modern society. Often, crimes scenes get recorded through low-resolution CCTV cameras limiting the efficiency of video anomaly detection (VAD) models. Despite substantial p…
Video Anomaly DetectionFoodMonitor: Benchmarking MLLMs for Explainable Compliance Analysis
As AI-powered compliance monitoring becomes increasingly important in public governance and industrial safety, the ability to provide verifiable evidence and traceable accountability signals is essential. However, existi…
Video Anomaly DetectionBinary ClassificationCoReVAD: A Contextual Reasoning Framework for Training-Free Video Anomaly Detection
Existing Video Anomaly Detection (VAD) methods typically rely on task-specific training, leading to strong domain dependency and high training costs. Moreover, most existing methods output only scalar anomaly scores, pro…
Video Anomaly DetectionBounding-Box Trajectories Matter for Video Anomaly Detection
Video anomaly detection is critical for public safety and security, yet remains highly challenging despite extensive research due to large variations in appearance, viewpoint, and scene dynamics. Among existing approache…
Video Anomaly DetectionPose EstimationLATERN: Test-Time Context-Aware Explainable Video Anomaly Detection
Vision-language models (VLMs) have recently emerged as a promising paradigm for video anomaly detection (VAD) due to their strong visual reasoning ability and natural language-based explainability. In this paper, we aim …
Video Anomaly DetectionVisual Reasoning