Optimized multiple object tracking with conformalized graph neural network and narwhal optimizer for embedded system IoT and mobile edge computing

This work presents a novel Multiple Object Visual Tracking (MOT) approach integrating Conformalized Graph Neural Network (CF-GNN) with Narwhal Optimizer (NO) for enhanced tracking accuracy and efficiency in IoT and Mobile Edge Computing (MEC) applications. Addressing challenges like noise, occlusion, and high computational complexity, the method employs Robust Double-Weighted Guided Image Filter (RDWGIF) for noise reduction, Improved YOLO v5 with Residual Group Attention Network (RGA-Net) for detection, and Dual-Branch Geometric Attention Network (DBGANet) for optimized feature extraction. Achieving 99.9% accuracy, 99.83% precision, 99.85% recall, and 99.88% F1-score, along with reduced power consumption (4.44 PC) and a 99% AUC, this method outperforms existing tracking solutions, making it highly effective for resource-constrained edge environments. While the proposed method demonstrates exceptional performance on the MOT16 dataset, its generalizability to more diverse or complex real-world scenarios and datasets requires further investigation, and scalability may be challenged in ultra-constrained hardware environments.