Title: A novel framework of video condensation and video retrieval process using hybrid meta-heuristic development with YOLO-based anomaly detection

Authors: Suhandas; G. Santhosh Kumar

Addresses: Department of Electronics and Communication Engineering, A J Institute of Engineering and Technology, NH-66, Kottara Chowki, Mangaluru, Karnataka 575006, India ' Department of Electronics and Communication Engineering, East West College of Engineering Bengaluru, CA Site-13, Major Akshay Girish Kumar Rd., Sector A, Yelahanka Satellite Town, Yelahanka New Town, Bengaluru, Karnataka 560064, India

Abstract: Visual surveillance systems have presently got the attention of various researchers. Anomaly detection is considered as a challenging issue that reduces the accuracy rate in the video retrieval system with the video surveillance data. To resolve this, a novel method is proposed for the video condensation and retrieval model. The extracted frames are given as input to the YoloV5 model, where the anomalies objects are detected. Here, the condensed video is retrieved using anomaly aware EOHLO-DGRU-MSF (AA-EOHLO-DGRU-MSF). Then, the parameters in DenseNet and GRU are optimised by using equilibrium optimiser-assisted hybrid leader optimisation (EOHLO) algorithm for attaining optimal results. Finally, the multi-similarity function (MSF) between the features in the database and query video is considered. Finally, the performance is evaluated and measured with diverse metrics. Contrary to other approaches, the proposed work outperforms the detection of video and retrieval of video.

Keywords: video condensation; video retrieval; anomaly detection; DenseNet; gated recurrent unit; equilibrium optimiser assisted hybrid leader optimisation; YOLOv5 model; multi-similarity function; MSF.

DOI: 10.1504/IJISTA.2024.140950

International Journal of Intelligent Systems Technologies and Applications, 2024 Vol.22 No.3, pp.281 - 312

Received: 24 Feb 2023
Accepted: 09 May 2023
Published online: 04 Sep 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article