Video Anomaly Detection

71 papers with code • 7 benchmarks • 10 datasets

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Most implemented papers

Adversarially Learned One-Class Classifier for Novelty Detection

khalooei/ALOCC-CVPR2018 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.

Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection

talreiss/accurate-interpretable-vad 1 Dec 2022

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.

Unsupervised Traffic Accident Detection in First-Person Videos

MoonBlvd/tad-IROS2019 2 Mar 2019

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 6 Apr 2020

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

tianyu0207/RTFM ICCV 2021

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 Temporal Regularity in Video Sequences

tnybny/Frame-level-anomalies-in-videos CVPR 2016

Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene.

Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction

zhangzilongc/MMR 5 Apr 2023

In this paper, to bridge this gap, we propose the Aero-engine Blade Anomaly Detection (AeBAD) dataset, consisting of two sub-datasets: the single-blade dataset and the video anomaly detection dataset of blades.

Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention

2023-MindSpore-4/Code4 28 Sep 2023

Our approach takes into account snippet-level encoded features without the supervision of pseudo labels.

Future Frame Prediction for Anomaly Detection -- A New Baseline

stevenliuwen/ano_pred_cvpr2018 28 Dec 2017

To predict a future frame with higher quality for normal events, other than the commonly used appearance (spatial) constraints on intensity and gradient, we also introduce a motion (temporal) constraint in video prediction by enforcing the optical flow between predicted frames and ground truth frames to be consistent, and this is the first work that introduces a temporal constraint into the video prediction task.

An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos

santiagxf/ContrastiveLearning 9 Jan 2018

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.