Video Anomaly Detection
78 papers with code • 11 benchmarks • 13 datasets
Datasets
Most implemented papers
Adversarially Learned One-Class Classifier for Novelty Detection
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
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
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
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.
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention
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
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
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.