Abnormal Event Detection In Video

12 papers with code • 2 benchmarks • 3 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

Most implemented papers

Real-world Anomaly Detection in Surveillance Videos

WaqasSultani/AnomalyDetectionCVPR2018 CVPR 2018

To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i. e. the training labels (anomalous or normal) are at video-level instead of clip-level.

Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection

talreiss/accurate-interpretable-vad 1 Dec 2022

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.

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.

A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video

lilygeorgescu/AED 27 Aug 2020

Following the standard formulation of abnormal event detection as outlier detection, we propose a background-agnostic framework that learns from training videos containing only normal events.

Abnormal event detection on BMTT-PETS 2017 surveillance challenge

gauraviitg/BMTT-PETS-2017-surveillance-challenge IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2017

Next, features are extracted from each frame using a convolutional neural network (CNN) that is trained to classify between normal and abnormal frames.

Generative Neural Networks for Anomaly Detection in Crowded Scenes

tianwangbuaa/VAE-for-abnormal-event-detection 29 Oct 2018

Security surveillance is critical to social harmony and people's peaceful life.

Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video

fjchange/object_centric_VAD CVPR 2019

Most existing approaches formulate abnormal event detection as an outlier detection task, due to the scarcity of anomalous data during training.

Weakly and Partially Supervised Learning Frameworks for Anomaly Detection

DegardinBruno/human_self_learning_anomaly 23 Jul 2020

The main objective is to provide several solutions to the mentioned problems, by focusing on analyzing previous state-of-the-art methods and presenting an extensive overview to clarify the concepts employed on capturing normal and abnormal patterns.

Anomaly Detection in Video via Self-Supervised and Multi-Task Learning

lilygeorgescu/AED-SSMTL CVPR 2021

To the best of our knowledge, we are the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture.