About

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

Benchmarks

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Subtasks

Datasets

Greatest papers with code

Real-world Anomaly Detection in Surveillance Videos

CVPR 2018 WaqasSultani/AnomalyDetectionCVPR2018

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.

ACTIVITY RECOGNITION ANOMALY DETECTION IN SURVEILLANCE VIDEOS MULTIPLE INSTANCE LEARNING SEMI-SUPERVISED ANOMALY DETECTION

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

CVPR 2019 fjchange/object_centric_VAD

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

ABNORMAL EVENT DETECTION IN VIDEO CLASSIFICATION OUTLIER DETECTION

Learning Temporal Regularity in Video Sequences

CVPR 2016 tnybny/Frame-level-anomalies-in-videos

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.

SEMI-SUPERVISED ANOMALY DETECTION

Weakly and Partially Supervised Learning Frameworks for Anomaly Detection

23 Jul 2020DegardinBruno/human_self_learning_anomaly

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.

ABNORMAL EVENT DETECTION IN VIDEO ANOMALY DETECTION IN SURVEILLANCE VIDEOS MULTIPLE INSTANCE LEARNING SELF-SUPERVISED LEARNING

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

27 Aug 2020lilygeorgescu/AED

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 IN VIDEO OUTLIER DETECTION