Anomaly Detection In Surveillance Videos
36 papers with code • 5 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Anomaly Detection In Surveillance Videos
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ShanghaiTech Campus
UCF-Crime
XD-Violence
UBI-Fights
VFP290K
CamNuvem Dataset
Most implemented papers
Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding
The proposed approach uses both abnormal and normal video clips during the training phase which is developed in the multiple instance framework where we treat video as a bag and video clips as instances in the bag.
Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision
Violence detection has been studied in computer vision for years.
Weakly and Partially Supervised Learning Frameworks for Anomaly Detection
DegardinBruno/human_self_learning_anomaly
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The main objective is to provide several solutions to the mentioned problems, by focusing on analyzing previous state-of-the-art methods and presenting an extensive overview to clarify the concepts employed on capturing normal and abnormal patterns.
Localizing Anomalies from Weakly-Labeled Videos
ktr-hubrt/WSAL
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In addition, in order to fully utilize the spatial context information, the immediate semantics are directly derived from the segment representations.
Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate
kevaldoshi17/Prediction-based-Video-Anomaly-Detection-
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Motivated by these research gaps, we propose an online anomaly detection method in surveillance videos with asymptotic bounds on the false alarm rate, which in turn provides a clear procedure for selecting a proper decision threshold that satisfies the desired false alarm rate.
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
To the best of our knowledge, we are the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture.
Iterative weak/self-supervised classification framework for abnormal events detection
DegardinBruno/human_self_learning_anomaly
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The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works.
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
fjchange/MIST_VAD
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CVPR 2021
Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations.
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning
wanboyang/Anomaly_AR_Net_ICME_2020
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Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration.
Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance Video
The intelligent surveillance system urgently needs the real-time machine recognition of abnormal events to solve the extremely uneven human supervision resource and digital cameras.
