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Abnormal Event Detection In Video

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Abnormal Event Detection In Video is a challenging task in computer vision, as the definition of what an abnormal event looks like depends very much on the context. For instance, a car driving by on the street is regarded as a normal event, but if the car enters a pedestrian area, this is regarded as an abnormal event. A person running on a sports court (normal event) versus running outside from a bank (abnormal event) is another example. Although what is considered abnormal depends on the context, we can generally agree that abnormal events should be unexpected events that occur less often than familiar (normal) events

Source: Unmasking the abnormal events in video

Image: Ravanbakhsh et al

논문

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Abnormal Event Detection In Video

논문

Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies

Enhanced semi-supervised stamping process monitoring with physically-informed feature extraction

Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures

Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs

BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection

Deep evolving semi-supervised anomaly detection

PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series

SADDE: Semi-supervised Anomaly Detection with Dependable Explanations

Directional anomaly detection

Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling

Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection

Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data

Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Image

Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor Signals

MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly Detection

Machine learning-based identification of Gaia astrometric exoplanet orbits

Improving Interpretability of Scores in Anomaly Detection Based on Gaussian-Bernoulli Restricted Boltzmann Machine

Semi-Supervised Health Index Monitoring with Feature Generation and Fusion

NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation

Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market

Label-based Graph Augmentation with Metapath for Graph Anomaly Detection

Semi-Supervised Anomaly Detection for the Determination of Vehicle Hijacking Tweets

Future Video Prediction from a Single Frame for Video Anomaly Detection

ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition

RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision

AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies

On Diffusion Modeling for Anomaly Detection

AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling

SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs

Reconstruction Error-based Anomaly Detection with Few Outlying Examples

AGAD: Adversarial Generative Anomaly Detection

Zero-shot domain adaptation of anomalous samples for semi-supervised anomaly detection

Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection

EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies

Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption

Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks