SOTAVerified

Abnormal Event Detection In Video

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

Papers

Showing 110 of 17 papers

TitleStatusHype
An Attribute-based Method for Video Anomaly DetectionCode1
Normalizing Flows for Human Pose Anomaly DetectionCode1
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection0
Learnable Locality-Sensitive Hashing for Video Anomaly DetectionCode0
Iterative weak/self-supervised classification framework for abnormal events detectionCode1
Anomaly Detection in Video via Self-Supervised and Multi-Task LearningCode1
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in VideoCode1
Weakly and Partially Supervised Learning Frameworks for Anomaly DetectionCode1
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in VideoCode0
Generative Neural Networks for Anomaly Detection in Crowded ScenesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GMMAUC0.91Unverified
2Sultani et al.AUC0.89Unverified
3Sultani et al.AUC0.79Unverified
4s2-VAEAUC0.61Unverified
5LSTM-VAEAUC0.54Unverified
6Adversarial GeneratorAUC0.53Unverified
7Hasan et al.AUC0.53Unverified
#ModelMetricClaimedVerifiedStatus
1AI-VADAUC99.1Unverified
2Background-Agnostic FrameworkAUC98.7Unverified
3SSMTLAUC97.5Unverified
4Adversarial GeneratorAUC97.4Unverified