Video Anomaly Detection
75 papers with code • 7 benchmarks • 10 datasets
Benchmarks
These leaderboards are used to track progress in Video Anomaly Detection
Datasets
ShanghaiTech Campus
CUHK Avenue
UBnormal
AeBAD
CHAD
GoodsAD
CamNuvem Dataset
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
•
•
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
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
•
•
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.
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
•
•
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
Daniel00008/WS-VAD-mindspore
•
•
Our approach takes into account snippet-level encoded features without the supervision of pseudo labels.
Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection
We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection, a novel video anomaly detection methodology that combines pseudo-anomalies, dynamic anomaly weighting, and a distinction loss function to improve detection accuracy.
Future Frame Prediction for Anomaly Detection -- A New Baseline
stevenliuwen/ano_pred_cvpr2018
•
•
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.
