Design of an Improved Model for Anomaly Detection in CCTV Systems Using Multimodal Fusion and Attention-Based Networks
Traditional approaches for video analysis often misdefine anomalies; they usually rely on single-modality input and have inadequate management of complex temporal patterns. This paper resolves these limitations by proposing a comprehensive scheme for multimodal Closed-Circuit Television (CCTV) video...
Saved in:
| Main Authors: | V. Srilakshmi, Sai Babu Veesam, Mallu Shiva Rama Krishna, Ravi Kumar Munaganuri, Dulam Devee Sivaprasad |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10876563/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Leveraging Feedback and Causality-Enriched Multimodal Context for Predictive Maintenance
by: Apostolos Giannoulidis, et al.
Published: (2025-01-01) -
Surprisal-based algorithm for detecting anomalies in categorical data
by: Ossama Cherkaoui, et al.
Published: (2025-06-01) -
Improved anomaly diagnosis of production facilities by combining Autoencoder with spectral characteristics
by: Fuki SAKA, et al.
Published: (2025-03-01) -
Anomaly Detection in IoMT Environment Based on Machine Learning: An Overview
by: Peyman Vafadoost Sabzevar, et al.
Published: (2024-12-01) -
Unsupervised Anomaly Detection for Volcanic Deformation in InSAR Imagery
by: Robert Popescu, et al.
Published: (2025-06-01)