Anomaly Detection In Surveillance Videos

24 papers with code • 4 benchmarks • 3 datasets

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Most implemented papers

Real-world Anomaly Detection in Surveillance Videos

WaqasSultani/AnomalyDetectionCVPR2018 CVPR 2018

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

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 Memory-guided Normality for Anomaly Detection

cvlab-yonsei/MNAD 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

lilygeorgescu/AED 27 Aug 2020

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.

Abnormal event detection on BMTT-PETS 2017 surveillance challenge

gauraviitg/BMTT-PETS-2017-surveillance-challenge IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2017

Next, features are extracted from each frame using a convolutional neural network (CNN) that is trained to classify between normal and abnormal frames.

007: Democratically Finding The Cause of Packet Drops

behnazak/Vigil-007SourceCode 20 Feb 2018

Network failures continue to plague datacenter operators as their symptoms may not have direct correlation with where or why they occur.

MIST: Multiple Instance Spatial Transformer Network

ubc-vision/mist 26 Nov 2018

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.

Anomaly Detection in Video Sequence with Appearance-Motion Correspondence

nguyetn89/Anomaly_detection_ICCV2019 17 Aug 2019

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

nguyetn89/Anomaly_detection_BMVC2019 17 Aug 2019

In this paper, we propose a deep convolutional neural network (CNN) for anomaly detection in surveillance videos.