Anomaly Detection In Surveillance Videos
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 7
- 보존된 평가 기록
- 62
"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)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ekosman/AnomalyDetectionCVPR2018-Pytorch
https://github.com/fluque1995/tfm-anomaly-detection
https://github.com/ktr-hubrt/WSAL
https://github.com/lilygeorgescu/AED-SSMTL
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
https://github.com/louisYen/S3R
https://github.com/m-3lab/awesome-visual-sensory-anomaly-detection
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
https://github.com/modadundun/ProDisc-VAD
https://github.com/nguyetn89/Anomaly_detection_BMVC2019
https://github.com/nguyetn89/Anomaly_detection_ICCV2019
Anomaly Detection in Video Sequence with Appearance-Motion Correspondence
https://github.com/seominseok0429/Real-world-Anomaly-Detection-in-Surveillance-Videos-pytorch
https://github.com/tianyu0207/CCD
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
https://github.com/ubc-vision/mist
https://github.com/wanboyang/Anomaly_AR_Net_ICME_2020
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.