CNN Based Vehicle Counting with Virtual Coil in Traffic Surveillance Video

This paper presents an efficient method of vehicle counting based on convolutional neural network (CNN) with virtual coils. Within virtual coils, foreground is obtained by background substraction. Vehicle is then detected by voting of virtual coil sub-regions. To deal with vehicle cross-lane cases, a cascade classifier combining connected component analysis (CCA) and CNN is adopted. Experiments are carried out on seven real traffic videos. The proposed approach works well on recognizing cross-lane vehicles, achieving above 90% accuracy with real-time processing speed.