In Intelligent Transportation Systems the identification and tracking of vehicles play an important role in enhancing traffic management, security, and overall road safety. Traditional means for vehicle re-identification rely solely on video-based systems which are not resilient to harsh environment conditions, suffer from visual obstructions, and are facing other challenges. To address these shortcomings and provide a more robust solution, alternative methods can be employed. This study addresses the gap in vehicle re-identification accuracy under harsh environmental conditions and visual obstructions faced by traditional video-based systems by integrating magnetic sensors into the road surface. The essence of this study revolves around a comprehensive comparison of various algorithms employed for feature extraction from registered magnetic field distortions. These distortions are treated as transient time series and various distance metrics are applied to calculate their similarity. Useful features are extracted and their classification performance is compared using a single neighbor classifier also taking into account calculation time. The validation experiments demonstrate the efficacy of presented approach in extracting critical features that hold the potential for successfully re-identifying same vehicles. For tested subset up to 90 % re-identification accuracy can be reached. The main contribution of this work involves determining which magnetic sensor axis to use—whether single or in combination—and identifying the most effective methods for feature extraction from the registered magnetic field distortions.
