Comparative Analysis of Background Subtraction and CNN Algorithms for Mid-Block Traffic Data Collection and Classification

Ubaid Illahi, Mohammad Shafi Mir
2020 International journal of mathematical, engineering and management sciences  
Classification of vehicles in the traffic stream is a pre-requisite for planning and designing the facilities for road-users. Considering the importance and gaining popularity of automated systems in this field, the aim of this article is to compare two algorithms- one using the Background Subtraction (BS) technique and the other using Convolutional Neural Network (CNN) with a primary focus on an increased number of vehicle classifications. To check the reliability of these algorithms, the
more » ... ts produced were validated against the data obtained from Kachkoot Toll Plaza, India. The results were analyzed using drop-line diagrams and confusion matrices. The overall efficiency of the CNN-based algorithm (0.98) was found to be better than the BS-based algorithm (0.95). The comparison presented in this paper will be useful for transportation professionals and agencies.
doi:10.33889/ijmems.2020.5.6.107 fatcat:6xoeja6kubbo3izqz3xkas3eny