Hybrid deep learning framework for ‎enhanced target trackingin video ‎surveillance using CNN and DRNN-GWO

Authors

  • Thupakula Bhaskar Department of Computer Engineering, Sanjivani College of Engineering, Kopargaon, Maharashtra, India
  • K. Sathish Department of CSE, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India
  • D. Rosy Salomi Victoria Department of Computer Science and Engineering, Chennai Institute of Technology, Chennai, Tamil Nadu, India
  • Er.Tatiraju. V. Rajani Kanth Senior Manager, TVR Consulting Services Private Limited, Hyderabad, Telangana, India
  • Uma Patil Department of CSE, PCET'S & NMVPM'S Nutan College of Engineering and Research, Pune, Maharashtra, India
  • Naveen Mukkapati Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
  • Sanjeevkumar Angadi Department of CSE, PCET'S & NMVPM'S Nutan College of Engineering and Research, Pune, Maharashtra, India
  • P Karthikeyan Department of ECE, Velammal College of Engineering and Technology, Madurai, Tamil Nadu 625009, India‎
  • vidhya R G Department of ECE, HKBK College of Engineering, Bangalore, India

How to Cite

Bhaskar , T. ., Sathish, K. . ., Victoria , D. R. S. ., Kanth , E. V. R. ., Patil , U. ., Mukkapati , N. ., Angadi , S. ., Karthikeyan , P. ., & R G, vidhya. (2025). Hybrid deep learning framework for ‎enhanced target trackingin video ‎surveillance using CNN and DRNN-GWO. International Journal of Basic and Applied Sciences, 14(1), 208-215. https://doi.org/10.14419/wddeck70

Received date: March 21, 2025

Accepted date: April 29, 2025

Published date: May 7, 2025

DOI:

https://doi.org/10.14419/wddeck70

Keywords:

Convolutional Neural Network; Computer Vision Target Tracking; Grey Wolf Optimization; Deep Recurrent Neural Network; ‎Video Surveillance

Abstract

The growing demand for advanced security solutions has driven significant progress in video surveillance technologies in recent ‎years. A critical component of modern surveillance systems is the ability to accurately track and monitor targets in dynamic ‎environments. In this paper, we present a computer vision-based target-tracking system designed to enhance the efficiency of video ‎surveillance operations. The proposed approach employs hybrid deep learning algorithms for the detection and tracking of targets ‎within video frames. Initially, recorded video footage from surveillance cameras is input into the system, where each frame ‎undergoes preprocessing to enhance quality. A Convolutional Neural Network (CNN) is then utilized to extract spatial features from ‎the preprocessed frames, enabling the precise identification and localization of objects. The CNN also detects regions of interest and ‎labels identified objects (e.g., persons, vehicles). We introduce a novel algorithm that combines the strengths of Deep Recurrent ‎Neural Networks (DRNN) and Grey Wolf Optimization (GWO), referred to as DRNN-GWO. The DRNN module captures spatial ‎and temporal dependencies within the frames to predict the future positions of tracked objects, while the GWO algorithm optimizes ‎the hyperparameters of the DRNN to further enhance tracking performance. The proposed framework was implemented in Python. ‎Experimental results demonstrated outstanding performance, achieving a target tracking accuracy of 99.12%, a recall of 98.75%, a ‎precision of 99.27%, and an F-measure of 99%‎.

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How to Cite

Bhaskar , T. ., Sathish, K. . ., Victoria , D. R. S. ., Kanth , E. V. R. ., Patil , U. ., Mukkapati , N. ., Angadi , S. ., Karthikeyan , P. ., & R G, vidhya. (2025). Hybrid deep learning framework for ‎enhanced target trackingin video ‎surveillance using CNN and DRNN-GWO. International Journal of Basic and Applied Sciences, 14(1), 208-215. https://doi.org/10.14419/wddeck70

Received date: March 21, 2025

Accepted date: April 29, 2025

Published date: May 7, 2025