Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/125534
Title: | Multi-property tensor-based learning for abnormal event detection |
Authors: | Bakalos, Nikolaos Doulamis, Nikolaos Doulamis, Anastasios Makantasis, Konstantinos |
Keywords: | Video surveillance -- Data processing Event processing (Computer science) Image processing -- Data processing Tensor algebra |
Issue Date: | 2022-10 |
Publisher: | Springer International Publishing |
Citation: | Bakalos, N., Doulamis, N., Doulamis, A., & Makantasis, K. (2022, October). Multi-property Tensor-Based Learning for Abnormal Event Detection. International Symposium on Visual Computing, San Diego. 325-335. |
Abstract: | In this paper, we propose a novel abnormal event detection scheme for video surveillance systems using an unsupervised learning process. Our contribution includes intra and inter property feature encoding to reduce information redundancy within (intra) and across (inter) image features. Intra property encoding is carried out using convolutional auto-encoders. Inter-property encoding is performed using an unsupervised tensor-based learning mode to handle the dimensionality issue arising in cases when different properties are inter-related together. Comprehensive experiments are performed on two benchmarks:Avenue, and ShanghaiTech. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/125534 |
Appears in Collections: | Scholarly Works - FacICTAI |
Files in This Item:
File | Description | Size | Format | |
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Multi property tensor based learning for abnormal event detection 2022.pdf Restricted Access | 1.9 MB | Adobe PDF | View/Open Request a copy |
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