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Multiple-Instance-Based-Video-Anomaly-Detection-Using-Deep-Temporal-Encoding-Decoding
Implementation of the paper "Multiple Instance-Based Video Anomaly Detection Using Deep Temporal Encoding-Decoding" (https://www.sciencedirect.com/science/article/pii/S0957417422020978) The paper is accepted for publication in Expert Systems with Applications Journal I will keep update this page. I will upload the test model once the paper get published..-->
<p align="center"> <img src="https://github.com/AmmarKamoona/Multiple-Instance-Based-Video-Anomaly-Detection-Using-Deep-Temporal-Encoding-Decoding/blob/main/images/proposed_network.png" width="1024"> </p>Multiple-Instance-Based-Video-Anomaly-Detection-Using-Deep-Temporal-Encoding-Decoding Results and Comparisons
- AUC Performance on UCF-Crime 80.10 % AUC of the ROC curve -Comparison provided against Hassan et al., Lu et al., Sultani et al., Zaheer et al., Zhong et al., and SRF.
AUC Performance on ShanghaiTech
- 89.14 % AUC of the ROC curve
- Comparison provided against Zaheer et al., Zhong et al., and SRF.
#Please cite the paper using the following
@article{KAMOONA2023119079,
title = {Multiple instance-based video anomaly detection using deep temporal encoding–decoding},
journal = {Expert Systems with Applications},
volume = {214},
pages = {119079},
year = {2023},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2022.119079},
url = {https://www.sciencedirect.com/science/article/pii/S0957417422020978},
author = {Ammar Mansoor Kamoona and Amirali Khodadadian Gostar and Alireza Bab-Hadiashar and Reza Hoseinnezhad},
}