DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network

Author:

Thakare Kamalakar Vijay1,Raghuwanshi Yash1,Dogra Debi Prosad1,Choi Heeseung2,Kim Ig-Jae2

Affiliation:

1. Indian Institute of Technology,Bhubaneswar,Odisha,India,752050

2. Artificial Intelligence and Robotics Institute, Korea Institute of Science and Technology,Seoul,Republic of Korea,02792

Funder

Korea Institute of Science and Technology

National Research Foundation

Publisher

IEEE

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1. Suspicious activities detection using spatial–temporal features based on vision transformer and recurrent neural network;Journal of Ambient Intelligence and Humanized Computing;2024-05-29

2. A Review Work: Human Action Recognition in Video Surveillance Using Deep Learning Techniques;Informatics and Automation;2024-03-28

3. Anomaly detection in video surveillance: a supervised inception encoder approach;Multimedia Tools and Applications;2024-02-26

4. A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

5. Multimedia datasets for anomaly detection: a review;Multimedia Tools and Applications;2023-12-13

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