An automatic traffic density estimation using Single Shot Detection (SSD) and MobileNet-SSD. (April 2019)

• Record Type: Journal Article
• Authors: Biswas, Debojit; Su, Hongbo; Wang, Chengyi; Stevanovic, Aleksandar; Wang, Weimin
• Is Part Of: Physics and chemistry of the earth. Volume 110(2019)
• Page Start: 176
• Page End: 184
• Publication Date: 2019-04
• Subjects: Traffic density -- Single shot detection -- MobileNet -- Convolution neural network -- Feature maps -- Visual Geometry Group; Geophysics -- Periodicals; Geochemistry -- Periodicals; Earth sciences -- Periodicals; Geodesy -- Periodicals; Astrophysics -- Periodicals; 550
• DOI: 10.1016/j.pce.2018.12.001
• Languages: English
• ISSNs: 1474-7065

Abstract

Abstract: Traffic density estimation is a very important component of an automated traffic monitoring system. Traffic density estimation can be used in a number of traffic applications – from congestion identification to macroscopic traffic control in urban environment. In this paper, we implemented Single Shot Detection (SSD) and MobileNet-SSD to estimate traffic density. SSD is capable of handling different shape, size and view angle of the objects. MobileNet-SSD is a cross-trained model from SSD to MobileNet architecture, which is faster than SSD. In this study, we show a key application area for the SSD and MobileNet-SSD framework. The advantages and shortcomings of the SSD and MobileNet-SSD framework were analyzed using fifty-nine individual traffic cameras. In addition, we compared the two algorithms with manually estimated density. The SSD framework shows significant potential in the field of traffic density estimation. SSD achieved 92.97% average detection accuracy in our experiment. On the other hand, the MobileNet-SSD achieved 79.30% average detection accuracy. Highlights: Real time traffic density estimation by traffic cameras and deep learning algorithms. Car counting using SSD and MobileNet-SSD. Quantitative analysis of the algorithms based on number of training data vs accuracy. Performance and speed comparison of SSD and MobileNet-SSD.