Cognitive Video Surveillance Management in Hierarchical Edge Computing System with Long Short-Term Memory Model. [PDF]
Ugli DBR, Kim J, Mohammed AFY, Lee J.
europepmc +1 more source
YOLOv12 for Human Object Detection in Real-time Video Surveillance Systems
This research discusses the application of the YOLO (You Only Look Once) model to detect human objects in real-time video surveillance systems. This model was developed in response to the increasing need for efficiency and accuracy in video surveillance ...
Yohanes Bowo Widodo +2 more
doaj +1 more source
Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities. [PDF]
Ingle PY, Kim YG.
europepmc +1 more source
Anomaly detection using edge computing in video surveillance system: review. [PDF]
Patrikar DR, Parate MR.
europepmc +1 more source
Tracking Missing Person in Large Crowd Gathering Using Intelligent Video Surveillance. [PDF]
Nadeem A +7 more
europepmc +1 more source
Classic video compression methods usually suffer from long encode time and requires large memories, making it hard to deploy on edge devices; thus, video compressive sensing, which requires less resources during encoding, is receiving more attention.
Lisha Gao +5 more
doaj +1 more source
State-of-the-art violence detection techniques in video surveillance security systems: a systematic review. [PDF]
Omarov B +4 more
europepmc +1 more source
Application of region-based video surveillance in smart cities using deep learning. [PDF]
Zahra A +4 more
europepmc +1 more source
Smart Video Surveillance System Based on Edge Computing. [PDF]
Cob-Parro AC +4 more
europepmc +1 more source
Semi-Supervised Anomaly Detection in Video-Surveillance Scenes in the Wild. [PDF]
Sarker MI +4 more
europepmc +1 more source

