Abnormal Event Detection In Video
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Abnormal Event Detection In Video is a challenging task in computer vision, as the definition of what an abnormal event looks like depends very much on the context. For instance, a car driving by on the street is regarded as a normal event, but if the car enters a pedestrian area, this is regarded as an abnormal event. A person running on a sports court (normal event) versus running outside from a bank (abnormal event) is another example. Although what is considered abnormal depends on the context, we can generally agree that abnormal events should be unexpected events that occur less often than familiar (normal) events
Source: Unmasking the abnormal events in video
Image: Ravanbakhsh et al
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Fraunhofer-AISEC/ARGUE
공식 코드https://github.com/Fraunhofer-AISEC/R2-AD2
공식 코드https://github.com/MahsaMesgaran/GFCN
공식 코드https://github.com/antoine-spahr/SELF-TAUGHT-SEMI-SUPERVISED-ANOMALY-DETECTION
공식 코드Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays
https://github.com/aoudsung/tri-crlad
공식 코드https://github.com/cool-xuan/anoonly
공식 코드AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies
https://github.com/cool-xuan/imbalanced_sam
공식 코드ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition
https://github.com/cvlab-yonsei/MNAD
공식 코드https://github.com/d10andy/sad
공식 코드https://github.com/donghao51/nng-mix
공식 코드NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
https://github.com/esa/gaia-astrometric-exoplanet-orbit-ml
공식 코드Machine learning-based identification of Gaia astrometric exoplanet orbits
https://github.com/jzc777/mapl
공식 코드MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly Detection
https://github.com/laurarkart/Physical-Anomalous-Trajectory-or-Motion-PHANTOM-Dataset
공식 코드Approaches Toward Physical and General Video Anomaly Detection
https://github.com/lcs-crr/path
공식 코드https://github.com/lilygeorgescu/AED
공식 코드A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
https://github.com/lukasruff/Deep-SAD-PyTorch
공식 코드https://github.com/m-code-space/sadde
공식 코드SADDE: Semi-supervised Anomaly Detection with Dependable Explanations
https://github.com/mala-lab/prenet
공식 코드https://github.com/missinghwan/MSAD
공식 코드Label-based Graph Augmentation with Metapath for Graph Anomaly Detection
https://github.com/missinghwan/mgad
공식 코드Label-based Graph Augmentation with Metapath for Graph Anomaly Detection
https://github.com/nikitazozoulenko/kernel-timeseries-anomaly-detection
공식 코드Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection
https://github.com/openvinotoolkit/anomalib
공식 코드GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
https://github.com/orhir/stg-nf
공식 코드https://github.com/takahashihiroshi/deep-puad
공식 코드Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
https://github.com/talreiss/accurate-interpretable-vad
공식 코드https://github.com/tbw162/purigan
공식 코드https://github.com/vicliv/dte
공식 코드https://github.com/xuhongzuo/rosas
공식 코드RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision
https://github.com/zugexiaodui/llshforvad
공식 코드Learnable Locality-Sensitive Hashing for Video Anomaly Detection
https://github.com/zzr728/sagan
공식 코드https://github.com/2023-MindSpore-1/ms-code-138
https://github.com/DegardinBruno/human_self_learning_anomaly
https://github.com/Eacient/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/EighteenSprings/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/Emyyr/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.