Conference
Multi-property tensor-based learning for abnormal event detection
| Title: | Multi-property tensor-based learning for abnormal event detection |
|---|---|
| Authors: | Bakalos, Nikolaos, Doulamis, Nikolaos, Doulamis, Anastasios, Makantasis, Konstantinos, International Symposium on Visual Computing ISVC 2022 |
| Publisher Information: | Springer International Publishing |
| Publication Year: | 2022 |
| Collection: | University of Malta: OAR@UM / L-Università ta' Malta |
| Subject Terms: | Video surveillance -- Data processing, Event processing (Computer science), Image processing -- Data processing, Tensor algebra |
| Description: | 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. ; peer-reviewed |
| Document Type: | conference object |
| Language: | English |
| Relation: | https://www.um.edu.mt/library/oar/handle/123456789/125534 |
| DOI: | 10.1007/978-3-031-20713-6_25 |
| Availability: | https://www.um.edu.mt/library/oar/handle/123456789/125534 https://doi.org/10.1007/978-3-031-20713-6_25 |
| Rights: | info:eu-repo/semantics/restrictedAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. |
| Accession Number: | edsbas.5E36BC33 |
| Database: | BASE |
| DOI: | 10.1007/978-3-031-20713-6_25 |
|---|