Title: Ensemble CNN model with novel optimisation technique for video content detection

Authors: Sita M. Yadav; Sandeep M. Chaware

Addresses: Department of Computer Engineering, Pimpri Chinchwad College of Engineering, India; Army Institute of Technology, Savitribai Phule Pune University, Pune, India ' Department of Computer Engineering, JSPM's Rajarshi Shahu College of Engineering, Savitribai Phule Pune University, Pune, India

Abstract: This research develops and implements a CNN-BiLSTM with chaser prairie wolf optimisation (CPW) model for video content analysis. Initially, the input is collected from the CAMVID and DAVIS datasets, the video is first been read. The optimised YOLO-4 model is proposed for detecting the objects from the video. The hybrid optimisation algorithm is developed from the characteristics of Albus and Falcon, and the role of the optimiser is to train the YOLO model. Then, in order to achieve enhanced performance for the multiclass object classification from videos, the identified objects are subjected to classification using a deep learning model employing the suggested CNN-coupled LSTM model. Additionally, the chaser priori wolf optimisation is used to enhance the deep learning classifier's training, which improves convergence rates. Based on the video content analysis model achievements, at training percentage (TP) 90, the accuracy is 95.75%, sensitivity is 97.30%, and specificity is 96.88% for D1, similarly based on D2 the accuracy is 97.77%, sensitivity is 99.00%, and specificity is 98.90%.

Keywords: hybrid optimisation algorithm; chaser priori optimisation; object detection; object classification; CNN-coupled LSTM.

DOI: 10.1504/IJCVR.2026.154148

International Journal of Computational Vision and Robotics, 2026 Vol.16 No.4, pp.472 - 496

Received: 19 Jan 2023
Accepted: 08 Feb 2024
Published online: 15 Jun 2026 *

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