Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model

Detection and Localization of Abnormalities in Surveillance Video Using Timerider-Based Neural Network

(2021) S Veluchamy et al.   COMPUTER JOURNAL

Modified Deep Reinforcement Learning with Efficient Convolution Feature for Small Target Detection in VHR Remote Sensing Imagery

(2021) Shuai Liu et al.   ISPRS International Journal of Geo-Information

Video anomaly detection and localization via Gaussian Mixture Fully Convolutional Variational Autoencoder

(2020) Yaxiang Fan et al.   COMPUTER VISION AND IMAGE UNDERSTANDING

Video anomaly detection and localization using motion-field shape description and homogeneity testing

(2020) Xinfeng Zhang et al.   PATTERN RECOGNITION

Automatic object tracking in optimized UAV video

(2019) Ancy A. Micheal et al.   JOURNAL OF SUPERCOMPUTING

Region-based scalable smart system for anomaly detection in pedestrian walkways

(2019) B.S. Murugan et al.   COMPUTERS & ELECTRICAL ENGINEERING

Intelligent fault detection of high voltage line based on the Faster R-CNN

(2019) Xusheng Lei et al.   MEASUREMENT

Online clinical decision support system using optimal deep neural networks

(2019) Lakshmanaprabu S.K. et al.   APPLIED SOFT COMPUTING

Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: A deep reinforcement learning approach

(2019) Yu Ding et al.   ADVANCED ENGINEERING INFORMATICS

Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes

(2018) Mohammad Sabokrou et al.   COMPUTER VISION AND IMAGE UNDERSTANDING

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

(2017) Shaoqing Ren et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Video anomaly detection and localisation based on the sparsity and reconstruction error of auto-encoder

(2016) M. Sabokrou et al.   ELECTRONICS LETTERS

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE