Vehicle classification is a crucial task owing to vehicles' diverse and intricate features, such as edges, colors, shadows, corners, and textures. The accurate classification of vehicles enables their detection and identification on roads and facilitates the development of an electronic tollcollection system for smart cities. Furthermore, vehicle classification is useful for traffic signal control strategy. However, achieving accurate vehicle classification poses significant challenges due to the limited processing time for real-time applications, image resolution, illumination variations in the video, and other interferences. This study proposes a method for automated automobile detection, recognition, and classification using statistics derived from approximately 11,000 images. We employ SURF-based detection and different classifiers to categorize vehicles into three groups. The Traffic Management System (TMS) is crucial for studying mobility and smart cities. Our study achieves a high automobile classification rate of 91% with the medium Gaussian Support Vector Machine (SVM) classifier. The paper's main objective is to analyze five object classifiers for vehicle recognition: Decision Tree, Discriminant Analysis, SVM, K-Nearest Neighbor Classifier (KNN), and Ensemble Classifier. In the discussion section, we present the limitations of our work and provide insights into future research directions.
Trivedi J, Devi MS, Dave D. Review Paper on Intelligent Traffic Control system using Computer Vision for Smart City”. Vol. 8, Int. Jour. of Sci. & Engi. Res. 2017. p. 14–7.
2.
Russo F, Rindone C, Panuccio P. European plans for the smart city: from theories and rules to logistics test case. Vol. 24, European Planning Studies. 2016. p. 1709–26.
3.
Croce A, Musolino G, Rindone C, Vitetta A. Transport System Models and Big Data: Zoning and Graph Building with Traditional Surveys, FCD and GIS. Vol. 8, ISPRS International Journal of Geo-Information. p. 187.
4.
Hardin PJ, Thomson CN. Fast Nearest Neighbor Classification Methods for Multispectral Imagery. Vol. 44, The Professional Geographer. 1992. p. 191–202.
5.
Ghosh AK. On Nearest Neighbor Classification Using Adaptive Choice of k. Vol. 16, Journal of Computational and Graphical Statistics. 2007. p. 482–502.
6.
Breiman L. Random Forests”. Machine Learning. 2001;45:5-32,.
7.
Breiman L. Random Forests. Machine Learning. 2001;45:5–32.
8.
Loh W. Regression Trees with Unbiased Variable Selection and Interaction Detection". Statistica Sinica. 2002;12:361–86.
9.
Paensuwan N, Yokoyama A, Verma SC, Nakachi Y. Application of Decision Tree Classification to the Probabilistic TTC Evaluation. Vol. 1, Journal of International Council on Electrical Engineering. 2011. p. 323–30.
10.
Wen X, Shao L, Xue Y, Fang W. A Rapid Learning Algorithm for Vehicle Classification. Information Sciences. 2015;295:395-406,.
11.
Kim JJB, J., Kim E. A Novel On-Road Vehicle Detection Method Using ΠHOG. IEEE Transactions on Intelligent Transportation Systems. 2015;16(6):3414-29,.
12.
Stocker M, Silvonen P, Rönkkö M, Kolehmainen M. Detection and Classification of Vehicles by Measurement of Road-Pavement Vibration and by Means of Supervised Machine Learning. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations. 2016;20(2):125-37,.
13.
Arinaldi A, Pradana JA, Gurusinga AA. Detection and classification of vehicles for traffic video analytics. Vol. 144, Procedia Computer Science. 2018. p. 259–68.
14.
Atiya A. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Vol. 16, IEEE Transactions on Neural Networks. 2005. p. 781–781.
15.
Sarikan SS, Ozbayoglu AM, Zilci O. Automated Vehicle Classification with Image Processing and Computational Intelligence. Vol. 114, Procedia Computer Science. 2017. p. 515–22.
16.
Gorges C, Öztürk K, R L. Road Classification for Two-Wheeled Vehicles. Vehicle System Dynamics. 2018;56(8):1289-1314,.
17.
Bautista MC, Dy AC, Manalac IM, Orbe AR, Cordel M. Convolutional neural network for vehicle detection in low-resolution traffic videos. IEEE Region. 2016;10 Symposium:277-281,.
18.
Bansal D, Khanna K, Chhikara R, Kumar Dua R, Malhotra R. Classification of Magnetic Resonance Images using Bag of Features for Detecting Dementia”. Procedia Computer Science. 2020;167:131-137,.
19.
Pranata YD, Wang KC, Wang JC, Idram I, Lai JY, Liu JW, et al. Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Vol. 171, Computer Methods and Programs in Biomedicine. 2019. p. 27–37.
20.
Sykora P, Kamencay P, Hudec R. Comparison of SIFT and SURF Methods for Use on Hand Gesture Recognition based on Depth Map. Vol. 9, AASRI Procedia. 2014. p. 19–24.
21.
Ignat A, Pavaloi I. Keypoint Selection Algorithm for Palmprint Recognition with SURF”, 25th Inter. Conf on Knowledge-Based and Intelligent Information & Engineering Systems, Procedia Computer Science. 2021;192:270-280,.
22.
Anzid H, Le Goic G, Mammass D, Bekkari A, Mansouri A. Multimodal Images Classification using Dense SURF, Spectral Information and Support Vector Machine”. In: Second International Conference on Intelligent Computing in Data Sciences, Procedia Computer Science. 2019. p. 107-115,.
23.
LoweDavid G. Distinctive Image Features from Scale-Invariant Keypoints”. International Journal of Computer Vision. 2004;60(2):91–110.
24.
Bay H, Tuytelaars T, Van Gool L. SURF: Speeded Up Robust Features. Computer Vision – ECCV 2006. 2006. p. 404–17.
25.
26.
27.
Trivedi J, Devi MS, Dave D. Vehicle classification using the convolution neural network approach. Scientific Journal of Silesian University of Technology Series Transport. 2021;112:201–9.
28.
Trivedi J, Devi MS, Dhara D. Vehicle Counting Module Design in Small Scale for Traffic Management in Smart City. 2018 3rd International Conference for Convergence in Technology (I2CT). 2018.
Citation
Copyright
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.
