Dual-Modality Deep Feature-based Anomaly Detection for Video Surveillance-Reference-Cited by-同舟云学术

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Dual-Modality Deep Feature-based Anomaly Detection for Video Surveillance

Published:2023-09-24 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)
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Author:

Bhatt Parth Lalitkumar¹,Shah Dhruva¹,Silver Christopher²,Zhang Wandong³,Akilan Thangarajah⁴

Affiliation:

1. Lakehead University,Dept. of Computer Science,Thunder Bay,Canada

2. Lakehead University,Dept. of Electrical and Computer Engineering,Thunder Bay,Canada

3. Western University,Dept. of Electrical and Computer Enggineering,London,Canada

4. Lakehead University,Dept. of Software Engineering,Thunder Bay,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10288628/10288648/10288767.pdf?arnumber=10288767

Reference25 articles.

1. Multi-Scale Continuity-Aware Refinement Network for Weakly Supervised Video Anomaly Detection

2. Learning Causal Temporal Relation and Feature Discrimination for Anomaly Detection

3. Learning Temporal Regularity in Video Sequences

4. Anomaly Detection by Predicting Future Frames using Convolutional LSTM in Video Surveillance

5. Motion-aware feature for improved video anomaly detection;zhu,2019

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