Learning normal patterns via conv-LSTM for video anomaly detection using likelihood statistical texture feature representation in surveillance videos

E. Murali, A. C. Santha Sheela, M. Asha Paul (), V. Muthu and A. Yovan Felix
Additional contact information
E. Murali: Sathyabama Institute of Science and Technology
A. C. Santha Sheela: Sathyabama Institute of Science and Technology
M. Asha Paul: Vivit Academy
V. Muthu: Panimalar Engineering College
A. Yovan Felix: Sathyabama Institute of Science and Technology

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 12, No 1, 3862 pages

Abstract: Abstract An anomaly automatic detection system is a challenging issue since there is a non-deterministic assumption or definition about the abnormal events. To address this issue, this paper introduced the Likelihood Statistical Texture Feature Representation (LSTFR) method using CSR (Co-occurrence with Stationary occurrence Representation) to construct the spatial activity pattern using gray level co-occurrence matrix with likelihood estimation. Also, LSTFR is used to construct the composition histogram representation to learn the normal behaviour. The occurrence rate in a LSTFR is characterized by a histogram representation, which depends on the spatio-temporal information of the frames sequence. To efficiently classify the events using the LSTFT, this paper uses the Convolutional Long Short-Term Memory (conv-LSTM) where histogram representation of LSTFR is automatically modelled by the training with normal events. The proposed method is evaluated on four benchmark datasets: UMN, Subway, Avenue, and UCSD Ped2. The performance of LSTFR-ConvLSTM is assessed using EER and AUC-ROC, achieving superior results compared to existing anomaly detection approaches. Finally, the proposed results are compared with several existing algorithms.

Keywords: Abnormal event detection; Co-occurrence matrix; Likelihood estimation; Conv-LSTM (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-025-02892-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:16:y:2025:i:12:d:10.1007_s13198-025-02892-4

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-025-02892-4

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().