Video Anomaly Detection

40 papers with code • 0 benchmarks • 1 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.

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.

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.

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.

Latent Space Autoregression for Novelty Detection

aimagelab/novelty-detection 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

RomeroBarata/skeleton_based_anomaly_detection CVPR 2019

Appearance features have been widely used in video anomaly detection even though they contain complex entangled factors.

Attribute Restoration Framework for Anomaly Detection

FishSmile-syx/ITAE-Pytorch-Anomaly_Detection 25 Nov 2019

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.