Cr-EEFO_YOLOv12: Chronological Electric Eel Foraging Optimization-Driven Deep Learning for Multi-Object Detection and Tracking in Vehicle Driving Footage

Intelligent transportation systems depend on accurately detecting and tracking objects, such as pedestrians, vehicles, etc., from driving footage to make effective decisions. The conventional schemes rely on data collected from multiple vehicles for Multi-Object Detection and Tracking (MODT), which poses a great risk to user privacy and security. Federated Learning (FL) facilitates collaborative model training across distributed devices while maintaining data privacy. The existing techniques often suffer from generalization issues and low convergence. Hence, an efficient model, namely Chronological Electrical Eel Foraging Optimization Algorithm_You Only Look Once version 12 (Cr-EEFO_YOLOv12) is proposed for MODT in FL. In this approach, the Cr-EEFO metaheuristic is employed to optimize the parameters of YOLOv12, improving detection performance and convergence during distributed training. In the FL framework, various entities, like nodes and servers, are involved. The local training is performed by considering local data. The server updation is performed by data aggregation, and the global model is downloaded to the nodes. During training, the input video is subjected to frame extraction. Then, object segmentation is accomplished utilizing Modified Fuzzy C-means Clustering (MFCM). Afterwards, multiple object detection is done using YOLOv12, trained using Cr-EEFO. Lastly, multi-object tracking is done using an Unscented Kalman Filter (UKF). Cr-EEFO_YOLOv12 attained a True Positive Rate (TPR), Mean Square Error (MSE), accuracy, True Negative Rate (TNR), and mean Average Precision (mAP) of 97.568%, 0.067, 96.667%, 96.358%, and 83.889% indicating its effectiveness in improving detection reliability and learning performance compared to existing approaches, when evaluated on the Driving Video with Object Tracking dataset.