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Deep learning model based on cascaded autoencoders and one‐class learning for detection and localization of anomalies from surveillance videos

https://doi.org/10.1049/bme2.12064

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9781118705957.app4openalex-snapshot

Multivariate Gaussian Distribution

inferredcites →https://doi.org/10.1007/978-3-030-31756-0_4openalex-snapshot

Assessment of Autoencoder Architectures for Data Representation

inferredcites →https://doi.org/10.1007/978-3-319-46454-1_21openalex-snapshot

A Discriminative Framework for Anomaly Detection in Large Videos

inferredcites →https://doi.org/10.1007/978-3-319-59081-3_23openalex-snapshot

Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder

inferredcites →https://doi.org/10.1007/978-3-642-17080-5_21openalex-snapshot

A Survey of Recent Trends in One Class Classification

inferredcites →https://doi.org/10.1007/978-981-10-7245-1_45openalex-snapshot

Assessment of Object Detection Using Deep Convolutional Neural Networks

inferredcites →https://doi.org/10.1007/s11280-018-0582-1openalex-snapshot

Deep learning approaches for video-based anomalous activity detection

inferredcites →https://doi.org/10.1016/j.cviu.2013.06.007openalex-snapshot

An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions

inferredcites →https://doi.org/10.1016/j.cviu.2016.10.010openalex-snapshot

Detecting anomalous events in videos by learning deep representations of appearance and motion

inferredcites →https://doi.org/10.1016/j.icte.2021.11.004openalex-snapshot

Deep learning based detection and localization of road accidents from traffic surveillance videos

inferredcites →https://doi.org/10.1016/j.neucom.2014.06.011openalex-snapshot

Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts

inferredcites →https://doi.org/10.1016/j.neucom.2020.07.019openalex-snapshot

Scene perception guided crowd anomaly detection

inferredcites →https://doi.org/10.1016/j.patrec.2019.11.024openalex-snapshot

Integrating prediction and reconstruction for anomaly detection

inferredcites →https://doi.org/10.1016/j.procs.2018.05.144openalex-snapshot

Application of Deep Learning for Object Detection

inferredcites →https://doi.org/10.1017/cbo9780511981234.024openalex-snapshot

The Multivariate Gaussian Distribution

inferredcites →https://doi.org/10.1049/iet-cvi.2015.0271openalex-snapshot

Video anomaly detection using deep incremental slow feature analysis network

inferredcites →https://doi.org/10.1109/cvpr.2009.5206569openalex-snapshot

Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates

inferredcites →https://doi.org/10.1109/cvpr.2009.5206641openalex-snapshot

Abnormal crowd behavior detection using social force model

inferredcites →https://doi.org/10.1109/cvpr.2010.5539872openalex-snapshot

Anomaly detection in crowded scenes

inferredcites →https://doi.org/10.1109/cvpr.2012.6247917openalex-snapshot

Video anomaly detection based on local statistical aggregates

inferredcites →https://doi.org/10.1109/cvpr.2016.86openalex-snapshot

Learning Temporal Regularity in Video Sequences

inferredcites →https://doi.org/10.1109/iccv19984.2013openalex-snapshot

2013 IEEE International Conference on Computer Vision

inferredcites →https://doi.org/10.1109/icip.2016.7532491openalex-snapshot

Holistic features for real-time crowd behaviour anomaly detection

inferredcites →https://doi.org/10.1109/lsp.2015.2410031openalex-snapshot

Learning to Detect Anomalies in Surveillance Video

inferredcites →https://doi.org/10.1109/ncvpripg.2013.6776164openalex-snapshot

Real time anomaly detection in H.264 compressed videos

inferredcites →https://doi.org/10.1109/tifs.2013.2272243openalex-snapshot

Video Anomaly Search in Crowded Scenes via Spatio-Temporal Motion Context

inferredcites →https://doi.org/10.1109/tip.2017.2670780openalex-snapshot

Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes

inferredcites →https://doi.org/10.1109/tip.2017.2695105openalex-snapshot

Video Anomaly Detection With Compact Feature Sets for Online Performance

inferredcites →https://doi.org/10.1109/tits.2018.2835308openalex-snapshot

Deep Spatio-Temporal Representation for Detection of Road Accidents Using Stacked Autoencoder

inferredcites →https://doi.org/10.1109/tmm.2019.2929931openalex-snapshot

Video Anomaly Detection and Localization Based on an Adaptive Intra-Frame Classification Network

inferredcites →https://doi.org/10.1109/tpami.2007.70825openalex-snapshot

Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors

inferredcites →https://doi.org/10.1109/tpami.2013.111openalex-snapshot

Anomaly Detection and Localization in Crowded Scenes

inferredcites →https://doi.org/10.1109/wacv.2019.00206openalex-snapshot

Training Adversarial Discriminators for Cross-Channel Abnormal Event Detection in Crowds

inferredcites →https://doi.org/10.1145/1541880.1541882openalex-snapshot

Anomaly detection

inferredcites →https://doi.org/10.1145/3123266.3123451openalex-snapshot

Spatio-Temporal AutoEncoder for Video Anomaly Detection

inferredcites →https://doi.org/10.1145/3317640.3317644openalex-snapshot

Spatial Temporal Balanced Generative Adversarial AutoEncoder for Anomaly Detection

inferredcites →https://doi.org/10.3390/app9040757openalex-snapshot

An On-Line and Adaptive Method for Detecting Abnormal Events in Videos Using Spatio-Temporal ConvNet

inferredcites →https://doi.org/10.5244/c.31.139openalex-snapshot

Anomaly Detection using a Convolutional Winner-Take-All Autoencoder

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s10489-025-06797-4openalex-snapshot

Deep learning applied for abnormal human behavior recognition in video surveillance systems: A systematic review

inferred← citeshttps://doi.org/10.1007/s11042-023-14917-wopenalex-snapshot

Detection and localization of anomalies in video surveillance using novel optimization based deep convolutional neural network

inferred← citeshttps://doi.org/10.1007/s11042-025-20608-5openalex-snapshot

An optimized multi-scale convolutional autoencoder for efficient abnormal event detection using rgb, depth and optical flow data

inferred← citeshttps://doi.org/10.1007/s13735-026-00415-wopenalex-snapshot

Language-driven models for video anomaly detection: taxonomy, datasets, and future horizons

inferred← citeshttps://doi.org/10.1007/s41315-024-00400-8openalex-snapshot

RH-DATM: generic object tracking and distributed attention-based BiLSTM for violent activity detection

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2025.110749openalex-snapshot

SASTNet: Self-Attention-Enabled Spatio-Temporal Network for single scene video anomaly detection

inferred← citeshttps://doi.org/10.1016/j.image.2026.117553openalex-snapshot

Highly efficient anomaly detection in traffic surveillance videos with optimized interference-tolerant fast convergence zeroing neural network

inferred← citeshttps://doi.org/10.1049/bme2.12110openalex-snapshot

Detection of non‐suicidal self‐injury based on spatiotemporal features of indoor activities

inferred← citeshttps://doi.org/10.1049/tje2.12375openalex-snapshot

BrutNet: A novel approach for violence detection and classification using DCNN with GRU

inferred← citeshttps://doi.org/10.1109/access.2023.3237028openalex-snapshot

A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction

inferred← citeshttps://doi.org/10.1109/etecom66111.2025.11319136openalex-snapshot

DeepTriad: A Hybrid Deep Learning Framework for Video Anomaly Detection Using FlowNet, ResNet50, and RCNN

inferred← citeshttps://doi.org/10.1109/gcwot63882.2024.10805682openalex-snapshot

A Review of Recent Advances in Intelligent Neuromorphic Computing-Assisted Machine Learning for Automatic Anomaly Detection

inferred← citeshttps://doi.org/10.1109/iccpc55978.2022.10072291openalex-snapshot

Fight Detection in surveillance video dataset versus real time surveillance video using 3DCNN and CNN-LSTM

inferred← citeshttps://doi.org/10.1109/icdsns58469.2023.10245238openalex-snapshot

Detection of Abnormal Behavioral States in Student Learning Based on Video Surveillance

inferred← citeshttps://doi.org/10.1111/exsy.13640openalex-snapshot

Leveraging YOLOv5s with optimization‐based effective anomaly detection in pedestrian walkways

inferred← citeshttps://doi.org/10.1111/exsy.70013openalex-snapshot

A Review of Abnormal Behaviour Detection in Crowd for Video Surveillance: Advances and Trends, Datasets, Opportunities and Prospects

inferred← citeshttps://doi.org/10.1142/s0129156425403298openalex-snapshot

A Lightweight Deep Learning-Based Method for Anomalous Traffic Detection in Wireless Communication Networks

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