Video Anomaly Detection
40 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Video Anomaly Detection
CamNuvem Dataset
Most implemented papers
Adversarially Learned One-Class Classifier for Novelty Detection
khalooei/ALOCC-CVPR2018
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CVPR 2018
Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples.
Unsupervised Traffic Accident Detection in First-Person Videos
Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous driving and advanced driver assistance systems.
When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos
MoonBlvd/Detection-of-Traffic-Anomaly
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A new spatial-temporal area under curve (STAUC) evaluation metric is proposed and used with DoTA.
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.
Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection
talreiss/accurate-interpretable-vad
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Surprisingly, we find that this simple representation is sufficient to achieve state-of-the-art performance in ShanghaiTech, the largest and most complex VAD dataset.
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos
santiagxf/ContrastiveLearning
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Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning.
Latent Space Autoregression for Novelty Detection
aimagelab/novelty-detection
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CVPR 2019
Novelty detection is commonly referred to as the discrimination of observations that do not conform to a learned model of regularity.
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos
Appearance features have been widely used in video anomaly detection even though they contain complex entangled factors.
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.
Attribute Restoration Framework for Anomaly Detection
FishSmile-syx/ITAE-Pytorch-Anomaly_Detection
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We here propose to break this equivalence by erasing selected attributes from the original data and reformulate it as a restoration task, where the normal and the anomalous data are expected to be distinguishable based on restoration errors.
