https://github.com/AmmarKamoona/Multiple-Instance-Based-Video-Anomaly-Detection-Using-Deep-Temporal-Encoding-Decoding
공식 코드Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding
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"The goal of a practical anomaly detection system is to timely signal an activity that deviates normal patterns and identify the time window of the occurring anomaly. [It] can be considered as coarse level video understanding, which filters out anomalies from normal patterns." A critical task in video surveillance is detecting anomalous events such as traffic accidents, crimes or illegal activities. Anomalous events rarely occur as compared to normal activities. Hence the application of this task is to "alleviate the waste of labor and time, developing intelligent computer vision algorithms for automatic video anomaly detection".
(Credit: Real-world Anomaly Detection in Surveillance Videos)
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding
Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision
Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection
STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applications
Consistency-based Self-supervised Learning for Temporal Anomaly Localization
MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection
FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation
Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly Detection
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
Anomaly detection in surveillance videos using transformer based attention model
Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
A MIL Approach for Anomaly Detection in Surveillance Videos from Multiple Camera Views
Multi-scale Bottleneck Transformer for Weakly Supervised Multimodal Violence Detection
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Space
Aligning First, Then Fusing: A Novel Weakly Supervised Multimodal Violence Detection Method
Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.