Abstract
This paper proposes a traffic monitoring system that detects, tracks, and classifies multiple vehicles on the road in real time using various digital image processing techniques and the process of machine learning based on a convolutional neural network (CNN). With this system, a video camera is installed on the road, and calibration is used to obtain the projection equation of the actual road on the image plane. Several image processing techniques, such as background modeling, background extraction, edge detection, and object tracking, are used to develop and implement a prototype system. The proposed system also uses a transfer learning process that is more efficient than starting CNN from scratch. This maximizes training efficiency and increases prediction accuracy in vehicle classification. Preliminary experimental results demonstrate that multiple vehicle tracking and classification are possible while calculating vehicle speed. The ultimate goal of this study is to develop a single digital video camera system with embedded machine learning process that can monitor and distinguish multiple vehicles simultaneously in multiple lanes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adu-Gyamfi Y, Asare S, Sharma A, Titus T (2017) Automated vehicle recognition with deep convolutional neural networks. Transport Res Record J Transport Res Board 2645:113–122
Bouwmans T, Baf FEl, Vachon B (2008) Background modeling using mixture of Gaussians for foreground detection—a survey. Recent Patents Comput Sci 1(3):219–237
Buch N, Velastin SA, Orwell J (2011) A review of computer vision techniques for the analysis of urban traffic. IEEE Trans Intell Transport Syst 12(3):920–939
Canny JF (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698
Capobianco S, Facheris L, Cuccoli F, Marinai S (2018) Vehicle classification based on convolutional networks applied to FM-CW radar signals. Adv Intell Syst Comput 728:115–128
Cathey FW, Dailey DJ (2004) One-parameter camera calibration for traffic management cameras. In: IEEE intelligent transportation systems conference, Washington, D.C., pp.865-869
Cucchiara R, Piccardi M, Prati A (2003) Detecting moving objects, ghosts, and shadows in video streams. IEEE Trans Pattern Anal Mach Intell 25:1337–1342
Deng L, Yu D (2014) Deep learning: methods and applications. Now Publishers Inc., Boston
Dong Z, Wu Y, Pei M, Jia Y (2015) Vehicle type classification using a semisupervised convolutional neural network. IEEE Trans Intell Transport Syst 16(4):2247–2256
Duda RO, Hart PE (1972) Use of the Hough transform to detect lines and curves in pictures. Commun ACM 15:11–15
Fernandes AF, Oliveria MM (2008) Real-time line detection through an improved Hough transform voting scheme. Pattern Recogn 41:299–314
He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916
Kim H (2013a) Image processing based multiple vehicle monitoring system. Inf Int Interdiscip J 16(2B):1491–1496
Kim H (2013b) Detecting moving objects using background modeling and local binary patterns. Inf Int Interdiscip J 16(3B):2305–2310
Kim P, Lim K (2017) Vehicle type classification using bagging and convolutional neural network on multi view surveillance image. In: 2017 IEEE conference on computer vision and pattern recognition workshops, Honolulu, pp 41–46
Koga Y, Miyazaki H, Shibasaki R (2018) A CNN-based method of vehicle detection from aerial images using hard example mining. Remote Sens 10:1–21
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25(2):1106–1114
Manana M, Tu C, Owolawi PA (2017) A survey on vehicle detection based on convolution neural networks. In: 3rd IEEE international conference on computer and communications, Chengdu, pp 1751–1755
McFarlane NBJ, Schofield CP (1995) Segmentation and tracking of piglets in images. Mach Vis Appl 8:187–193
Mu K, Hui F, Zhao X (2016) Multiple vehicle detection and tracking in highway traffic surveillance video based on SIFT feature matching. J Inf Process Syst 12(2):183–195
Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th international conference on machine learning, pp 807–814
Piccardi M (2014) Background subtraction techniques: a review. In: IEEE international conference on systems, man and cybernetics, pp 3099–3104
Rother R (2002) A new approach to vanishing point detection in architectural environments. Image Vis Comput 20(9–10):647–655
Schoepflin TN, Dailey DJ (2003) Dynamic camera calibration of roadside traffic management cameras for vehicle speed estimation. IEEE Trans Intell Transport Syst 4(2):90–98
Sobral A, Vacavant A (2014) A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput Vis Image Underst 122:4–21
Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747–757
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kim, H. Multiple vehicle tracking and classification system with a convolutional neural network. J Ambient Intell Human Comput 13, 1603–1614 (2022). https://doi.org/10.1007/s12652-019-01429-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-019-01429-5