Vehicle Counting Quantitative Comparison Using Background Subtraction, Viola Jones and Deep Learning Methods

In this paper, vehicle counting is investigated using various machine methods on four datasets. Vehicle counting is needed to complete the data required for short term predictions using highway traffic model, which is in turn, applicable for road design and usage planning. The goal of this research is to show that automatic car counting using machine methods, can be obtained from utilizing existing CCTV image data or from better cameras. Then by applying quantitative evaluation, F1 and precision scores are obtained, so that a few recommendations can be given. Through numerous simulations, F1 scores ranging from 0.32 to 0.75 have been successfully obtained for one low resolution dataset, using Background Subtraction and Viola Jones methods, on existing CCTV data. It has been found also that Viola Jones method can improve F1 score, by about 39% to 56%, over Back Subtraction method. Furthermore, the use of Deep Learning especially YOLO has provided good results, with F1 scores ranging from 0.94 to 1 and its precision ranges from 97.37% to 100% involving three datasets of higher resolution. (Abstract)