Fine-Grained Vehicle Make and Model Recognition Framework Based on Magnetic Fingerprint

Fine-grained vehicle classification of make and model recognition is of practical significance and can be widely applied in the tasks such as traffic perception and control. To date, vision-based vehicle make and model recognition methods have improved through the years; however, they often fail in low-visibility scenes. This paper proposes a fine-grained vehicle model recognition framework based on magnetic fingerprints instead of videos from surveillance cameras. The magnetic fingerprint refers to the pattern of magnetic field data obtained when vehicles pass magnetic sensors, which have less sensitivity and more robustness towards low-visibility scenes than surveillance cameras. Specifically, the proposed framework first utilizes adversarial autoencoders to extract the magnetic fingerprint from the raw magnetic field data, keeping detailed information for fine-grained vehicle model recognition as complete as possible. Then, an AdaBoostSVM algorithm is used to identify vehicle models from the magnetic fingerprint and further alleviate the hard-negative issue to reduce false classifications. Massive experimental results show that the proposed fine-grained vehicle magnetic fingerprint/AdaBoostSVM method achieves state-of-the-art results on fine-grained vehicle make and model recognition. The average precision reached 94.3% and the average recall reached 94.2% in the dataset containing 36 vehicle models in total. Notably, the proposed method outperforms surveillance camera methods, especially in low-visibility scenes, making it a valuable addition to future intelligent transportation systems.