Papers Anomaly Detection In Surveillance Videos
“Anomaly Detection In Surveillance Videos” 태그가 달린 논문 68편 · 필터 해제
Human-Centric Anomaly Detection in Surveillance Videos Using YOLO-World and Spatio-Temporal Deep Learning
Anomaly detection in surveillance videos remains a challenging task due to the diversity of abnormal events, class imbalance, and scene-dependent visual clutter. To address these issues, we propose a robust deep learning…
Anomaly Detection In Surveillance VideosAnomaly ClassificationUnmasking Performance Gaps: A Comparative Study of Human Anonymization and Its Effects on Video Anomaly Detection
Advancements in deep learning have improved anomaly detection in surveillance videos, yet they raise urgent privacy concerns due to the collection of sensitive human data. In this paper, we present a comprehensive analys…
Anomaly Detection In Surveillance VideosVideo Anomaly DetectionDual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping
Federated weakly supervised video anomaly detection represents a significant advancement in privacy-preserving collaborative learning, enabling distributed clients to train anomaly detectors using only video-level annota…
Anomaly DetectionAnomaly Detection In Surveillance VideosEdge-computingFederated Learning+5Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly Detection
Most existing video anomaly detectors rely solely on RGB frames, which lack the temporal resolution needed to capture abrupt or transient motion cues, key indicators of anomalous events. To address this limitation, we pr…
Anomaly DetectionAnomaly Detection In Surveillance VideosVideo Anomaly DetectionVideo UnderstandingProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications
Weakly-supervised video anomaly detection (WS-VAD) using Multiple Instance Learning (MIL) suffers from label ambiguity, hindering discriminative feature learning. We propose ProDisc-VAD, an efficient framework tackling t…
Anomaly Detection In Surveillance VideosContrastive LearningMultiple Instance LearningSupervised Anomaly Detection+3CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos
Video Anomaly Detection (VAD) remains a fundamental yet formidable task in the video understanding community, with promising applications in areas such as information forensics and public safety protection. Due to the ra…
Anomaly DetectionAnomaly Detection In Surveillance VideosRepresentation LearningVideo Anomaly Detection+1STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applications
This paper presents a new method for anomaly detection in automated systems with time and compute sensitive requirements, such as autonomous driving, with unparalleled efficiency. As systems like autonomous driving becom…
Anomaly DetectionAnomaly Detection In Surveillance VideosAutonomous DrivingComputational EfficiencyAligning First, Then Fusing: A Novel Weakly Supervised Multimodal Violence Detection Method
Weakly supervised violence detection refers to the technique of training models to identify violent segments in videos using only video-level labels. Among these approaches, multimodal violence detection, which integrate…
Anomaly Detection In Surveillance VideosMultiple Instance LearningOptical Flow EstimationWeakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network
The widespread implementation of urban surveillance systems has necessitated more sophisticated techniques for anomaly detection to ensure enhanced public safety. This paper presents a significant advancement in the fiel…
Anomaly DetectionAnomaly Detection In Surveillance VideosMultiple Instance LearningSupervised Anomaly Detection+2MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance Videos
Detection of anomaly events is relevant for public safety and requires a combination of fine-grained motion information and contextual events at variable time-scales. To this end, we propose a Multi-Timescale Feature Lea…
Anomaly DetectionAnomaly Detection In Surveillance VideosSupervised Anomaly DetectionVideo Anomaly Detection+1Abnormal Event Detection In Videos Using Deep Embedding
Abnormal event detection or anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. Due to the lack of anomalous events at training time, anomaly detection requires …
Anomaly DetectionAnomaly Detection In Surveillance VideosDiversityEvent Detection+1A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos
Anomaly detection in videos is challenging due to the complexity, noise, and diverse nature of activities such as violence, shoplifting, and vandalism. While deep learning (DL) has shown excellent performance in this are…
Anomaly DetectionAnomaly Detection In Surveillance VideosFairnessTransfer LearningDistilling Aggregated Knowledge for Weakly-Supervised Video Anomaly Detection
Video anomaly detection aims to develop automated models capable of identifying abnormal events in surveillance videos. The benchmark setup for this task is extremely challenging due to: i) the limited size of the traini…
Anomaly DetectionAnomaly Detection In Surveillance VideosVideo Anomaly DetectionWeakly-supervised Video Anomaly DetectionMulti-scale Bottleneck Transformer for Weakly Supervised Multimodal Violence Detection
Weakly supervised multimodal violence detection aims to learn a violence detection model by leveraging multiple modalities such as RGB, optical flow, and audio, while only video-level annotations are available. In the pu…
Anomaly Detection In Surveillance VideosOptical Flow EstimationMULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
We propose a novel approach to video anomaly detection: we treat feature vectors extracted from videos as realizations of a random variable with a fixed distribution and model this distribution with a neural network. Thi…
Anomaly DetectionAnomaly Detection In Surveillance VideosDenoisingDensity Estimation+1BatchNorm-based Weakly Supervised Video Anomaly Detection
In weakly supervised video anomaly detection (WVAD), where only video-level labels indicating the presence or absence of abnormal events are available, the primary challenge arises from the inherent ambiguity in temporal…
Anomaly DetectionAnomaly Detection In Surveillance VideosVideo Anomaly DetectionWeakly-supervised Video Anomaly DetectionVALD-GAN: video anomaly detection using latent discriminator augmented GAN
The most crucial and difficult challenge for intelligent video surveillance is to identify anomalies in a video that comprises anomalous behavior or occurrences. The ambiguous definition of the anomaly makes the detectio…
Anomaly DetectionAnomaly Detection In Surveillance VideosVideo Anomaly DetectionSTemGAN: spatio-temporal generative adversarial network for video anomaly detection
Automatic detection and interpretation of abnormal events have become crucial tasks in large-scale video surveillance systems. The challenges arise from the lack of a clear definition of abnormality, which restricts the …
Anomaly DetectionAnomaly Detection In Surveillance VideosDecoderGenerative Adversarial Network+3A MIL Approach for Anomaly Detection in Surveillance Videos from Multiple Camera Views
Occlusion and clutter are two scene states that make it difficult to detect anomalies in surveillance video. Furthermore, anomaly events are rare and, as a consequence, class imbalance and lack of labeled anomaly data ar…
Anomaly DetectionAnomaly Detection In Surveillance VideosMultiple Instance LearningLearning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection
Video anomaly detection under weak supervision presents significant challenges, particularly due to the lack of frame-level annotations during training. While prior research has utilized graph convolution networks and se…
Anomaly DetectionAnomaly Detection In Surveillance VideosVideo Anomaly DetectionWeakly-supervised Anomaly Detection+1