Vehicle Classification using IPCP and EsKNN Algorithm for Surveillance Camera
DOI:
https://doi.org/10.31358/techne.v18i01.181Keywords:
vehicle classification, background subtraction, ensemble classifiers, intelligent transportation system, vehicles detectionAbstract
This paper presents a new approach vehicles detection and classification. In these works, we are creating a system to detect the vehicles and automatically classifying them into three desired classes as Car, Suv, and Truck. Datasets from changedetect.net were used for testing the method. The proposed approach combine Incremental Principle Component Pursuit (IPCP) background subtraction for vehicle detection and it complies with Ensemble subspace -Nearest Neighbor (EsKNN) classifier. A combination of background subtraction and classification of filtering background is used in order to focus on vehicle’s features extraction using HOG corner detections. Then, this feature extraction is classified using an ensemble-based classifiers technique. The choice of classifier depends on features data characteristics, which they were formed in certain unique distance shape stationary relative between classes. The proposed method is evaluated using three datasets of common highway surveillance video. Comparing with other direct detection and classification technique our method has achieved outstanding result. The proposed approach delivers the accuracy 96.5%, the highest among the tested methods. Experimental results show the outstanding performance of the proposed method.
Downloads
References
[2] T. Bouwmans, "Traditional and recent approaches in background modeling for foreground detection: An overview," Computer Science Review, Vols. 11-12, pp. 31-66, May 2014.
[3] P. Rodriuez and B. Wohlberg, "Incremental Principal Component Pursuit for Video Background Modeling," Journal Mathematical Imaging and Vision, vol. 55, no. 1, pp. 1-18, 2016.
[4] R. Zhao and X. Wang, "Counting Vehicles from Sematic Regions," IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 2, pp. 1016-1022, June 2013.
[5] P. Prommool, S. Auephanwiriyakul and N. Theera-Umpon, "Vision-based Automatic Vehicle Counting System Using Motion Estimation with Taylor Series Approximation," in 6th IEEE International Conference on Control System, Penang, Malaysia, 2016.
[6] C. Roopashree and T. Sateesh kumar, "Vehicle Detection and Counting," International Journal of Electrical and Electronics Engineers, vol. 07, no. 1, pp. 160-166, 2015.
[7] D. Li, B. Liang and W. Zhang, "Real-time Moving Vehicle Detection, Tracking, and Counting System Implemented with OpenCV," in IEEE conference on Computer Vision, Chinna, 2014.
[8] L. Unzueta, M. Nieto, A. Cortes, B. Javier, O. Otaegui and P. Sanchez, "Adaptive Multicue Background Subtraction for Robust Vehicle Counting and Classification," IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 2, pp. 527-540, June 2012.
[9] M. Liang, X. Huang, C. Chung-Hao, X. Chen and A. Tokuta, "Counting and Classification of Highway Vehicles by Regression Analysis," IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, vol. 16, no. 5, pp. 2878-2888, 2015.
[10] S. V. Huffel, "partial Singular Value Decomposition Algorithm," Journal of Computational and Applied Mathematics, vol. 33, no. 1, pp. 105-112, 1990.
[11] N. Goyette, P.-M. Jodoin, F. Porikli, J. Jonrad and P. Ishwar, "Changedetection.net: A new change detection bechmark dataset," 2014. [Online]. Available: www.changedetection.net. [Accessed 23 April 2017].
[12] E. Rosten and T. Drummond, "Machine Learning for High-Speed Corner Detection," Computer Vision – ECCV, vol. 3951, pp. 430-443, 2006.
[13] K. Mizuno, Y. Terachi, K. Takagi, S. Izumi, H. Kawaguci and M. Yoshimoto, "ARCHITECTURAL STUDY OF HOG FEATURE EXTRACTION PROCESSOR FOR REAL-TIME OBJECT DETECTION," in IEEE Workshop on Signal Processing Systems, Japan, 2012.
[14] A. Gul, A. Perpereglou, Z. Khan, O. Mahmoud, M. Miftahuddin, W. Adler and B. Lausen, "Ensemble of a subset of kNN classifiers," Adv Data Anal Classif, pp. 1-14, 2016.
