Real-Time Vehicle Detection and Location Tracking for Military Vehicles Using YOLOv8 and Multi-Object Tracking

Abstract

Intelligent transportation systems, traffic surveillance and smart city monitoring require accurate vehicle detection and tracking. Traditional methods of monitoring are usually based on either manual monitoring or GPS-based monitoring which may have issues with signal dependency and lack of scalability. This paper suggests a deep learning-based car surveillance system, which combines the YOLOv8 object detector and a multi-object tracking system to perform automated automobile detection and tracking. A prepared set of custom traffic image data (about 1,700 images) was divided into a training (80%) and validation (20%) sample and trained on the YOLOv8-Nano model to detect vehicles. Images had been resized to 640 × 640 resolution during training with 50 epochs of a batch size of 16 with transfer learning on pretrained weights. The trained detector had the accuracy of 0.81 with the recall of 0.74 and the mean Average Precision (mAP-0.5) of 0.79 on vehicle detection assignments. The detecting unit was further combined with tracking structure to retain vehicle identities between sequential frames to provide the capability of regular tracking of multi-object in traffic environment. The test outcomes show that the suggested system can work at about 40 frames per seconds (FPS) with the evaluation dataset and still retain a good tracking precision of about 0.76. Bounding boxes, tracking IDs and performance graphs are some of the visualization results, which support the efficiency of the methodology. The given framework can also be used to track the location of military vehicles in surveillance domains during the situations when the convoy movements or tactical vehicle location can be monitored automatically in GPS-denied or irregular conditions.