Skip to main content

Tipp

Weitere Kapitel dieses Buchs durch Wischen aufrufen

Erschienen in:
Buchtitelbild

2020 | OriginalPaper | Buchkapitel

A Structural Feature Based Automatic Vehicle Classification System at Toll Plaza

verfasst von : Vivek Singh, Amish Srivastava, Snehal Kumar, Rajib Ghosh

Erschienen in: 4th International Conference on Internet of Things and Connected Technologies (ICIoTCT), 2019

Verlag: Springer International Publishing

share
TEILEN

Abstract

Most of the existing toll collection systems in vehicle toll plazas in India are manual in nature. Automation of toll collection systems at toll plazas will make the system a lot faster and fraud-free. The primary task for building such a system is to classify the vehicles arriving at toll plazas because accordingly the amount of toll varies. Most of the existing works in this regard have focused on tracking and detecting of on-road vehicles, but very few of them tried to classify the vehicles. This article presents a novel machine learning based approach to detect vehicles arriving in toll plazas along with their types or classes. In this approach, various structural features are extracted from each vehicle before feeding those features to different classifiers. An exhaustive experiment has been performed on a large self-generated dataset using five different classifiers - Gaussian naive Bayes, Multinomial naive Bayes, Logistic regression, Random forest and Support Vector Classifier (SVC). An encouraging accuracy of 96.15% is obtained from the present system.
Literatur
1.
Zurück zum Zitat Tai, J.C., Tseng, S.T., Lin, C.P., Song, K.T.: Real-time image tracking for automatic traffic monitoring and enforcement applications. Image Vision Comput. 22(6), 485–501 (2004) CrossRef Tai, J.C., Tseng, S.T., Lin, C.P., Song, K.T.: Real-time image tracking for automatic traffic monitoring and enforcement applications. Image Vision Comput. 22(6), 485–501 (2004) CrossRef
2.
Zurück zum Zitat Michalopoulos, P.G.: Vehicle detection video through image processing: the Autoscope system. IEEE Trans. Veh. Technol. 40(1), 21–29 (1991) CrossRef Michalopoulos, P.G.: Vehicle detection video through image processing: the Autoscope system. IEEE Trans. Veh. Technol. 40(1), 21–29 (1991) CrossRef
3.
Zurück zum Zitat Park, S., Kim, T., Kang, S., Heon, K.: A novel signal processing technique for vehicle detection radar. In: IEEE MTT-S International Microwave Symposium Digest, Philadelphia, USA, pp. 607–610 (2003) Park, S., Kim, T., Kang, S., Heon, K.: A novel signal processing technique for vehicle detection radar. In: IEEE MTT-S International Microwave Symposium Digest, Philadelphia, USA, pp. 607–610 (2003)
4.
Zurück zum Zitat Wang, C., Thorpe, C., Suppe, A.: Ladar-based detection and tracking of moving objects from a ground vehicle at high speeds. In: IEEE IV2003 Intelligent Vehicles Symposium, Columbus, USA, pp. 416–421 (2003) Wang, C., Thorpe, C., Suppe, A.: Ladar-based detection and tracking of moving objects from a ground vehicle at high speeds. In: IEEE IV2003 Intelligent Vehicles Symposium, Columbus, USA, pp. 416–421 (2003)
5.
Zurück zum Zitat Chellappa, R., Qian, G., Zheng, Q.: Vehicle detection and tracking using acoustic and video sensors. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, Canada, pp. 793–796 (2004) Chellappa, R., Qian, G., Zheng, Q.: Vehicle detection and tracking using acoustic and video sensors. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, Canada, pp. 793–796 (2004)
6.
Zurück zum Zitat Kuehnle, A.: Symmetry-based recognition for vehicle rears. Pattern Recogn. Lett. 12(4), 249–258 (1991) CrossRef Kuehnle, A.: Symmetry-based recognition for vehicle rears. Pattern Recogn. Lett. 12(4), 249–258 (1991) CrossRef
7.
Zurück zum Zitat Zielke, T., Brauckmann, M., von Seelen, W.: Intensity and edge-based symmetry detection with an application to car-following. CVGIP: Image Underst. 58(2), 177–190 (1993) CrossRef Zielke, T., Brauckmann, M., von Seelen, W.: Intensity and edge-based symmetry detection with an application to car-following. CVGIP: Image Underst. 58(2), 177–190 (1993) CrossRef
8.
Zurück zum Zitat Crisman, J., Thorpe, C.: Color vision for road following. In: Cambridge Symposium on Advances in Intelligent Robotics Systems, Boston, USA, pp. 246–249 (1988) Crisman, J., Thorpe, C.: Color vision for road following. In: Cambridge Symposium on Advances in Intelligent Robotics Systems, Boston, USA, pp. 246–249 (1988)
9.
Zurück zum Zitat Buluswar, S.D., Draper, B.A.: Color machine vision for autonomous vehicles. Eng. Appl. Artif. Intell. 11(2), 245–256 (1988) CrossRef Buluswar, S.D., Draper, B.A.: Color machine vision for autonomous vehicles. Eng. Appl. Artif. Intell. 11(2), 245–256 (1988) CrossRef
10.
Zurück zum Zitat Guo, D., Fraichard, T., Xie, M., Laugier, C.: Color modeling by spherical influence field in sensing driving environment. In: IEEE Intelligent Vehicles Symposium 2000, Dearborn, USA, pp. 249–254 (2000) Guo, D., Fraichard, T., Xie, M., Laugier, C.: Color modeling by spherical influence field in sensing driving environment. In: IEEE Intelligent Vehicles Symposium 2000, Dearborn, USA, pp. 249–254 (2000)
11.
Zurück zum Zitat Mori, H., Charkai, N.: Shadow and rhythm as sign patterns of obstacle detection. In: IEEE International Symposium on Industrial Electronics Conference, Budapest, Hungary, pp. 271–277 (1993) Mori, H., Charkai, N.: Shadow and rhythm as sign patterns of obstacle detection. In: IEEE International Symposium on Industrial Electronics Conference, Budapest, Hungary, pp. 271–277 (1993)
12.
Zurück zum Zitat Dickmanns, E.D., Behringer, R., Dickmanns, D., Hildebrandt, T., Maurer, M., Thomanek, F., Schiehlen, J.: The seeing passenger car ‘VaMoRs-P’. In: Proceedings of the Intelligent Vehicles 1994 Symposium, France, Paris, pp. 24–26 (1994) Dickmanns, E.D., Behringer, R., Dickmanns, D., Hildebrandt, T., Maurer, M., Thomanek, F., Schiehlen, J.: The seeing passenger car ‘VaMoRs-P’. In: Proceedings of the Intelligent Vehicles 1994 Symposium, France, Paris, pp. 24–26 (1994)
13.
Zurück zum Zitat Bertozzi, M., Broggi, A., Castelluccio, S.: A real-time oriented system for vehicle detection. J. Syst. Archit. 43(1–5), 317–325 (1997) CrossRef Bertozzi, M., Broggi, A., Castelluccio, S.: A real-time oriented system for vehicle detection. J. Syst. Archit. 43(1–5), 317–325 (1997) CrossRef
14.
Zurück zum Zitat Matthews, N., An, P., Charnley, D., Harris, C.: Vehicle detection and recognition in greyscale imagery. Control Eng. Pract. 4(4), 473–479 (1996) CrossRef Matthews, N., An, P., Charnley, D., Harris, C.: Vehicle detection and recognition in greyscale imagery. Control Eng. Pract. 4(4), 473–479 (1996) CrossRef
15.
Zurück zum Zitat Sivaraman, S., ManubhaiTrivedi, M.: A general active-learning framework for on-road vehicle recognition and tracking. IEEE Trans. Intell. Transp. Syst. 11(2), 267–276 (2010) CrossRef Sivaraman, S., ManubhaiTrivedi, M.: A general active-learning framework for on-road vehicle recognition and tracking. IEEE Trans. Intell. Transp. Syst. 11(2), 267–276 (2010) CrossRef
16.
Zurück zum Zitat Chan, Y., Huang, S., Fu, L., Hsiao, P.: Vehicle detection under various lighting conditions by incorporating particle filter. In: IEEE Intelligent Transportation Systems Conference, Seattle, USA, pp. 534–539 (2007) Chan, Y., Huang, S., Fu, L., Hsiao, P.: Vehicle detection under various lighting conditions by incorporating particle filter. In: IEEE Intelligent Transportation Systems Conference, Seattle, USA, pp. 534–539 (2007)
17.
Zurück zum Zitat Tropartz, S., Horber, E., Gruner, K.: Experiences and results from vehicle classification using infrared overhead laser sensors at toll plazas in New York City. In: IEEE International Conference on Intelligent Transportation Systems, Tokyo, Japan, pp. 686–691 (1999) Tropartz, S., Horber, E., Gruner, K.: Experiences and results from vehicle classification using infrared overhead laser sensors at toll plazas in New York City. In: IEEE International Conference on Intelligent Transportation Systems, Tokyo, Japan, pp. 686–691 (1999)
18.
Zurück zum Zitat Abdelbaki, H.M., Hussain, K., Gelenbe, E.: A laser intensity image based automatic vehicle classification system. In: IEEE Intelligent Transportation Systems, Oakland, USA, pp. 460–465 (2001) Abdelbaki, H.M., Hussain, K., Gelenbe, E.: A laser intensity image based automatic vehicle classification system. In: IEEE Intelligent Transportation Systems, Oakland, USA, pp. 460–465 (2001)
19.
Zurück zum Zitat Ghosh, R., Thakre, S., Kumar, P.: A vehicle number plate recognition system using region-of-interest based filtering method. In: 2018 Conference on Information and Communication Technology (CICT 2018), Jabalpur, India, pp. 1–6 (2018) Ghosh, R., Thakre, S., Kumar, P.: A vehicle number plate recognition system using region-of-interest based filtering method. In: 2018 Conference on Information and Communication Technology (CICT 2018), Jabalpur, India, pp. 1–6 (2018)
Metadaten
Titel
A Structural Feature Based Automatic Vehicle Classification System at Toll Plaza
verfasst von
Vivek Singh
Amish Srivastava
Snehal Kumar
Rajib Ghosh
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-39875-0_1