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
Latest papers
BatchNorm-based Weakly Supervised Video Anomaly Detection
In the proposed BN-WVAD, we leverage the Divergence of Feature from Mean vector (DFM) of BatchNorm as a reliable abnormality criterion to discern potential abnormal snippets in abnormal videos.
A MIL Approach for Anomaly Detection in Surveillance Videos from Multiple Camera Views
In this paper, we tackle these typical problems of anomaly detection in surveillance video by combining Multiple Instance Learning (MIL) to deal with the lack of labels and Multiple Camera Views (MC) to reduce occlusion and clutter effects.
Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection
Additionally, we propose a Prompt-Enhanced Learning (PEL) module that integrates semantic priors using knowledge-based prompts to boost the discriminative capacity of context features while ensuring separability between anomaly sub-classes.
Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Space
To overcome this, we propose HyperVD, a novel framework that learns snippet embeddings in hyperbolic space to improve model discrimination.
Diversity-Measurable Anomaly Detection
FlappyPeggy/DMAD
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CVPR 2023
In this paper, to better handle the tradeoff problem, we propose Diversity-Measurable Anomaly Detection (DMAD) framework to enhance reconstruction diversity while avoid the undesired generalization on anomalies.
MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection
carolchenyx/mgfn
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Weakly supervised detection of anomalies in surveillance videos is a challenging task.
Normalizing Flows for Human Pose Anomaly Detection
Video anomaly detection is an ill-posed problem because it relies on many parameters such as appearance, pose, camera angle, background, and more.
Self-supervised Sparse Representation for Video Anomaly Detection
louisYen/S3R
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ECCV 2022 2022
Video anomaly detection (VAD) aims at localizing unexpected actions or activities in a video sequence.
Consistency-based Self-supervised Learning for Temporal Anomaly Localization
aimagelab/Consistency-based-Self-supervised-Learning-for-Temporal-Anomaly-Localization
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This work tackles Weakly Supervised Anomaly detection, in which a predictor is allowed to learn not only from normal examples but also from a few labeled anomalies made available during training.
Modality-Aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence Detection
In this paper, we analyze the modality asynchrony and undifferentiated instances phenomena of the multiple instance learning (MIL) procedure, and further investigate its negative impact on weakly-supervised audio-visual learning.
