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

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%‎.