Automated Object Detection and Classification using Krill Herd Algorithm with Deep Learning on Surveillance Videos
Abstract: In the domain of video surveillances, the implementation of deep learning (DL) for object detection and classification is developed as a game-changer. This paper introduces a comprehensive solution integrating the power of DL methods to handle these fundamental tasks. Leveraging state-of-the-art neural networks (NN), our technique allows consistent object identification and categorization within surveillance videos, providing improved security, real-time context awareness, and enriched decision-making abilities. This study develops an Automated Object Detection and Classification using Krill Herd Algorithm with Deep Learning (AODC-KHADL) technique on Surveillance Videos. The introduced AODC-KHADL method efficiently detects and classifies the objects into numerous categories. This technique starts with the incorporation of YOLO-v5, a recent object detection method popular for its excellent accuracy and speed for identifying objects in videos and images. For enhancing YOLO-v5's detection potential, we utilize Random Vector Functional Link (RVFL) classification, a multipurpose and robust machine learning (ML) approach. In this context, we present the Krill Herd Algorithm (KHA), a nature-inspired optimization method inspired by the collective behavior of krill swarms. By using extensive examination and assessment, we exhibit the model's capability in real-time video surveillance applications. The simulation values of the AODC-KHADL technique are tested on benchmark video and it is emphasized the higher performance of the AODC-KHADL system with other models.
Keywords: Object classification; Video surveillance; Object detection; Krill Herd Algorithm; Deep learning
How to Cite?: V. Saikrishnan, Dr. M. Karthikeyan, "Automated Object Detection and Classification using Krill Herd Algorithm with Deep Learning on Surveillance Videos", Volume 12 Issue 10, October 2023, International Journal of Science and Research (IJSR), Pages: 86-92, https://www.ijsr.net/getabstract.php?paperid=SR23929155916, DOI: https://dx.doi.org/10.21275/SR23929155916