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

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