Algorithms for Vehicle Classification
Surendra Gupte, Nikolaos P Papanikolopoulos
Report no. MnDOT 2000-27
Projects: Algorithms for Vehicle Classification
Topics: Traffic Modeling and Data
This report presents algorithms for vision-based detection and classification of vehicles in modeled at rectangular patches with certain dynamic behavior. The proposed method is based on the establishment of correspondences among blobs and vehicles, as the vehicles move through the image sequence. The system can classify vehicles into two categories, trucks and non-tucks, based on the dimensions of the vehicles. In addition to the category of each vehicle, the system calculates the velocities of the vehicles and generates counts of vehicles in each lane over a user-specified time interval, the total count of each type of vehicle, and the average velocity of each lane during this interval.