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Title: Suspicious Human Activity Recognition From Surveillance Videos Using Deep Learning

Journal Article · · IEEE Access
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4];  [5];  [6]; ORCiD logo [7]
  1. Department of Electrical Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia
  2. School of Management and Information Technology, De La Salle—,College of Saint Benilde, Manila, Philippines
  3. Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia
  4. College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
  5. Computer Science Department, Faculty of Computers &, Information Technology, University of Tabuk, Tabuk, Saudi Arabia
  6. Faculty of Management, Comenius University in Bratislava, Bratislava, Slovakia
  7. Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
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Sponsoring Organization:
USDOE Office of Electricity (OE), Advanced Grid Research & Development. Power Systems Engineering Research
OSTI ID:
2426973
Journal Information:
IEEE Access, Journal Name: IEEE Access Vol. 12; ISSN 2169-3536
Publisher:
Institute of Electrical and Electronics EngineersCopyright Statement
Country of Publication:
United States
Language:
English

References (24)

A survey on deep learning-based real-time crowd anomaly detection for secure distributed video surveillance journal June 2021
CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks journal August 2020
An optimized dense convolutional neural network model for disease recognition and classification in corn leaf journal August 2020
Skeleton‐based human activity recognition for elderly monitoring systems journal November 2017
Human activity recognition using 2D skeleton data and supervised machine learning journal October 2019
Convolutional neural networks (CNN) for indoor human activity recognition using Ubisense system conference May 2017
Large-Scale Video Classification with Convolutional Neural Networks conference June 2014
Convolutional Two-Stream Network Fusion for Video Action Recognition conference June 2016
Learning Activity Progression in LSTMs for Activity Detection and Early Detection conference June 2016
Real-World Anomaly Detection in Surveillance Videos conference June 2018
Unsupervised Anomaly Detection for Traffic Surveillance Based on Background Modeling conference June 2018
Detection of Real-world Fights in Surveillance Videos conference May 2019
Deep Learning Approach for Suspicious Activity Detection from Surveillance Video conference March 2020
Long-Term Temporal Convolutions for Action Recognition journal June 2018
Machine learning in video surveillance for fall detection conference May 2018
Detecting Video Surveillance Using VGG19 Convolutional Neural Networks journal January 2020
Anomalous Situations Recognition in Surveillance Images Using Deep Learning journal January 2023
Patient Monitoring by Abnormal Human Activity Recognition Based on CNN Architecture journal November 2020
An Online Continuous Human Action Recognition Algorithm Based on the Kinect Sensor journal January 2016
A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data journal May 2017
Human Activity Recognition via Hybrid Deep Learning Based Model journal January 2022
A New Approach for Abnormal Human Activities Recognition Based on ConvLSTM Architecture journal April 2022
Object Tracking and Suspicious Activity Identification during Occlusion journal January 2018
ConvGRU-CNN: Spatiotemporal Deep Learning for Real-World Anomaly Detection in Video Surveillance System journal January 2023

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