Abnormal Event Detection In Video
12 papers with code • 2 benchmarks • 4 datasets
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
ShanghaiTech
ShanghaiTech Campus
UCSD Ped2
UBI-Fights
Latest papers with no code
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection
The proposed end-to-end multi-stream architecture performs abnormal event detection with accuracy as high as 84. 48%, which is better than the performance of existing video anomaly detection methods.
Learnable Locality-Sensitive Hashing for Video Anomaly Detection
In this paper, we propose a novel distance-based VAD method to take advantage of all the available normal data efficiently and flexibly.
Cascaded Region-based Densely Connected Network for Event Detection: A Seismic Application
Because of the fact that some positive events are not correctly annotated, we further formulate the detection problem as a learning-from-noise problem.
Abnormal Event Detection in Videos using Generative Adversarial Nets
In this paper we address the abnormality detection problem in crowded scenes.
Unmasking the abnormal events in video
We propose a novel framework for abnormal event detection in video that requires no training sequences.
