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
Real-world Anomaly Detection in Surveillance Videos
To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i. e. the training labels (anomalous or normal) are at video-level instead of clip-level.
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
To address this issue, we introduce a novel and theoretically sound method, named Robust Temporal Feature Magnitude learning (RTFM), which trains a feature magnitude learning function to effectively recognise the positive instances, substantially improving the robustness of the MIL approach to the negative instances from abnormal videos.
Learning Memory-guided Normality for Anomaly Detection
cvlab-yonsei/MNAD
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CVPR 2020
To address this problem, we present an unsupervised learning approach to anomaly detection that considers the diversity of normal patterns explicitly, while lessening the representation capacity of CNNs.
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
Following the standard formulation of abnormal event detection as outlier detection, we propose a background-agnostic framework that learns from training videos containing only normal events.
ADNet: Temporal Anomaly Detection in Surveillance Videos
hibrahimozturk/temporal_anomaly_detection
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Additionally, we propose to use F1@k metric for temporal anomaly detection.
Abnormal event detection on BMTT-PETS 2017 surveillance challenge
Next, features are extracted from each frame using a convolutional neural network (CNN) that is trained to classify between normal and abnormal frames.
MIST: Multiple Instance Spatial Transformer Network
ubc-vision/mist
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We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts.
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection
jx-zhong-for-academic-purpose/GCN-Anomaly-Detection
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CVPR 2019
Remarkably, we obtain the frame-level AUC score of 82. 12% on UCF-Crime.
Anomaly Detection in Video Sequence with Appearance-Motion Correspondence
nguyetn89/Anomaly_detection_ICCV2019
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The training stage is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input.
Hybrid Deep Network for Anomaly Detection
In this paper, we propose a deep convolutional neural network (CNN) for anomaly detection in surveillance videos.
