Example：10.1021/acsami.1c06204 or Chem. Rev., 2007, 107, 2411-2502
Comparative Analysis of Background Subtraction and CNN Algorithms for Mid-Block Traffic Data Collection and Classification International Journal of Mathematical, Engineering and Management Sciences (IF), Pub Date : 2020-12-01, DOI: 10.33889/ijmems.2020.5.6.107 Ubaid Illahi, Mohammad Shafi Mir
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 algorithmsone 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 outputs 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. KeywordsComputer vision, Traffic data, Object detection, Background subtraction, Convolutional neural network.