Abstract
The idea of detection of moving objects and the concept of classification of moving objects is considered to be the important part of research in video processing and in real-time applications for surveillance and tracking of vehicles. In scientific terms, image processing is said to be any form of signal processing, where the input is an image and the output of image processing may taken be either an image or a set of characteristics or parameters related to the image. The proposed work of the paper is to detect and classify vehicles in a given video. It consists of two modules, first one uses GLOH algorithm for feature extraction and feature reduction. The second module classifies the vehicle from an input video frame using PCA. The final result is obtained by integrating the above-said modules.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Anagnostopoulos CNE, Anagnostopoulos IE, Psoroulas ID, Loumos V, Kayafas E (2008) License plate recognition from still Images and video sequences: a survey. Intell Trans Syst IEEE Trans 9:377–391. https://doi.org/10.1109/tits.2008.922938
Butzke M, Silva AG, Hounsell MS, Pillon MA (2008) Automatic recognition of vehicle attribute-color classification and logo segmentation. Hifen, Urugaiana, pp 32–62
Cao X, Wu C, Yan P, Li X (2011) Linear svm classification using boosting hog features for vehicle detection in low-altitude airborne videos. In: Proceedings of the 18th IEEE international conference on image processing
Zhang C, Chen X, Chen W (2006) A PCA-based vehicle classification framework. In: 22nd international conference on data engineering workshops
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110
Bagarinao E, Kurita T, Higashikubo M, Inayoshi H (2009) Adapting SVM image classifiers to changes in imaging conditions using incremental SVM: an application to car detection. In: Proceeding of 9th Asian conference on computer vision (ACCV), pp 363–372
Liu H (2009) Skew detection for complex document images using robust borderlines in both text and non-text regions. Sci Dir, Patt Recogn Lett
Kim KK, Kim KI, Kim JB, Kim HJ (2000) Learning-based approach for license plate recognition. In: Proceeding of IEEE workshop on neural networks for signal processing, vol 2, pp 614–623
Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Patt Anal Mach Intell 27(10):1615–1630
Juan L, Gwun O (2010) A comparison of SIFT, PCA-SIFT and SURF. Int J Image Process (IJIP) 3(4)
Sivaraman S, Manubhai Trivedi M (2010) A general active-learning framework for on-road vehicle recognition and tracking. IEEE Trans Intell Transport Syst 11(2):267–276
Vapnik V, Golowich S, Smola A (1997) Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems, Cambridge, MA, MIT Press, pp 281–287
Shao Y, Lunetta RS (2010) Comparison of support vector machine, neural network, and CART algorithms for the land cover classification using limited training data points. Environmental Protection Agency, USA
Yasser Arafat S, Saleem M, Afaq Hussain S (2009) Comparative analysis of invariant schemes for logo classification. In: Proceedings of international conference of emerging technologies (ICET), pp 256–261
Chen Z, Pears N, Freeman M, Austin J (2009) Road vehicle classification using support vector machines. Intell Comput Intell Syst
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Selvanayaki, K.S., Somasundaram, R., Shyamala Devi, J. (2019). Detection and Recognition of Vehicle Using Principal Component Analysis. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_97
Download citation
DOI: https://doi.org/10.1007/978-3-030-00665-5_97
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00664-8
Online ISBN: 978-3-030-00665-5
eBook Packages: EngineeringEngineering (R0)