Object-guided and motion-refined attention network for video anomaly detection
W Zhou, Y Li, C Zhao - 2022 IEEE International Conference on …, 2022 - ieeexplore.ieee.org
Video anomaly detection is challenging due to the lack of abnormal videos and ambiguity of
anomaly definition. Context information is important to identify anomalous events and can be …
anomaly definition. Context information is important to identify anomalous events and can be …
Abnormal event detection using deep contrastive learning for intelligent video surveillance system
The continuous developments of urban and industrial environments have increased the
demand for intelligent video surveillance. Deep learning has achieved remarkable …
demand for intelligent video surveillance. Deep learning has achieved remarkable …
Oe-ctst: Outlier-embedded cross temporal scale transformer for weakly-supervised video anomaly detection
Video anomaly detection in real-world scenarios is challenging due to the complex temporal
blending of long and short-length anomalies with normal ones. Further, it is more difficult to …
blending of long and short-length anomalies with normal ones. Further, it is more difficult to …
Toward video anomaly retrieval from video anomaly detection: New benchmarks and model
Video anomaly detection (VAD) has been paid increasing attention due to its potential
applications, its current dominant tasks focus on online detecting anomalies, which can be …
applications, its current dominant tasks focus on online detecting anomalies, which can be …
Unsupervised video anomaly detection based on similarity with predefined text descriptions
J Kim, S Yoon, T Choi, S Sull - Sensors, 2023 - mdpi.com
Research on video anomaly detection has mainly been based on video data. However,
many real-world cases involve users who can conceive potential normal and abnormal …
many real-world cases involve users who can conceive potential normal and abnormal …
Self-supervised attentive generative adversarial networks for video anomaly detection
Video anomaly detection (VAD) refers to the discrimination of unexpected events in videos.
The deep generative model (DGM)-based method learns the regular patterns on normal …
The deep generative model (DGM)-based method learns the regular patterns on normal …
3D-convolutional neural network with generative adversarial network and autoencoder for robust anomaly detection in video surveillance
As the surveillance devices proliferate, various machine learning approaches for video
anomaly detection have been attempted. We propose a hybrid deep learning model …
anomaly detection have been attempted. We propose a hybrid deep learning model …
DAM: Dissimilarity attention module for weakly-supervised video anomaly detection
Video anomaly detection under weak supervision is complicated due to the difficulties in
identifying the anomaly and normal instances during training, hence, resulting in non …
identifying the anomaly and normal instances during training, hence, resulting in non …
Anomaly detection with prototype-guided discriminative latent embeddings
Recent efforts towards video anomaly detection (VAD) try to learn a deep autoencoder to
describe normal event patterns with small reconstruction errors. The video inputs with large …
describe normal event patterns with small reconstruction errors. The video inputs with large …
A survey on deep learning techniques for video anomaly detection
JJP Suarez, PC Naval Jr - arXiv preprint arXiv:2009.14146, 2020 - arxiv.org
Anomaly detection in videos is a problem that has been studied for more than a decade.
This area has piqued the interest of researchers due to its wide applicability. Because of this …
This area has piqued the interest of researchers due to its wide applicability. Because of this …