Improved Robust Vehicle Detection and Identification Based on Single Magnetic Sensor

Abstract

Vehicle detection and identification techniques have been widely applied in traffic scene to acquire traffic information depending on various sensors, such as video camera, induction loop, and magnetic sensor. The quantity and category of vehicles are the key components of the intelligent transportation systems as they provide original data for further analysis. Compared with the inductive loop and video camera, magnetic sensor can measure magnetic field distortion caused by the movement of vehicles. The precise amount and category of a vehicle can be obtained through reasonable data analysis. A novel vehicle detection algorithm is proposed based on the short-term variance sequence transformed from raw magnetic signal. The parking-sensitive module is introduced to enhance the robustness and adaptability of a detection method. With abundant signal data, 42-D features are extracted from every vehicle signal comprising statistical features of whole waveform and short-term features of fragment signal. The Gradient Tree Boosting algorithm is employed to identify four vehicle categories. The effectiveness of the proposed approach is validated by the data collected at a freeway exit of Beijing. According to the experiential results based on 4507 vehicles, the vehicle detection algorithm proves to have 99.8% accurate rate and can be highly practical in site traffic environment. The 80.5% accuracy rate on vehicle identification approves the effectiveness of the proposed features on recognizing vehicles.