The real-time vehicle detection and counting plays a crucial role in traffic control. To collect traffic information continuously, the access to information from traffic video shows great importance and huge advantages compared with traditional technologies. However, most current algorithms are not adapted to the effects of undesirable environments, such as sudden changes in illumination, vehicle shadows, and complex urban traffic conditions, etc. To address these problems, a new vehicle detection and counting method was proposed in this paper. Based on a real-time background model, the problem of sudden illumination changes could be solved, while the vehicle shadows could be removed using a detection method based on motion. The vehicle counting was built on two types of ROIs—called Normative-Lane and Non-Normative-Lane—which could adapt to the complex urban traffic conditions, especially for non-normative driving. Results have shown that the methodology we proposed is able to count vehicles with 99.93% accuracy under the undesirable environments mentioned above. At the same time, the setting of the Normative-Lane and the Non-Normative-Lane can realize the detection of non-normative driving, and it is of great significance to improve the counting accuracy.
Real-Time System Based on Feature Extraction for Vehicle Detection and Classification