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HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway
https://doi.org/10.22146/ijccs.54050 Firnanda Al Islama Achyunda Putra(1*), Fitri Utaminingrum(2), Wayan Firdaus Mahmudy(3)
(1) Universitas Brawijaya
(2) Faculty of Computer Science, Universitas Brawijaya, Malang
(3) Faculty of Computer Science, Universitas Brawijaya, Malang
(*) Corresponding Author
Abstract
Autonomous car is a vehicle that can guide itself without human intervention. Various types of rudderless vehicles are being developed. Future systems where computers take over the art of driving. The problem is prior to being attention in an autonomous car for obtaining the high safety. Autonomous car need early warning system to avoid accidents in front of the car, especially the system can be used in the Highway location. In this paper, we propose a vision-based vehicle detection system for Autonomous car. Our detection algorithm consists of three main components: HOG feature extraction, KNN classifier, and vehicle detection. Feature extraction has been used to recognize an object such as cars. In this case, we use HOG feature extraction to detect as a car or non-car. We use the KNN algorithm to classify. KNN Classification in previous studies had quite good results. Car detected by matching about trining data with testing data. Trining data created by extract HOG feature from image 304 x 240 pixels. The system will produce a classification between car or non-car.
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[1] S. Kumar and D. Toshniwal, “A data mining framework to analyze road accident data,” J. Big Data, vol. 2, no. 1, 2015.
[2] D. Konstantinidis, T. Stathaki, V. Argyriou, and N. Grammalidis, “Building Detection Using Enhanced HOG-LBP Features and Region Refinement Processes,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 10, no. 3, pp. 888–905, 2017.
[3] S. Qi, J. Ma, J. Lin, Y. Li, and J. Tian, “Unsupervised Ship Detection Based on Saliency and S-HOG Descriptor From Optical Satellite Images,” IEEE Geosci. Remote Sens. Lett., vol. 12, no. 7, pp. 1451–1455, 2015.
[4] M. Bilal, A. Khan, M. U. K. Khan, and C. M. Kyung, “A Low-Complexity Pedestrian Detection Framework for Smart Video Surveillance Systems,” IEEE Trans. Circuits Syst. Video Technol., vol. 27, no. 10, pp. 2260–2273, 2017.
[5] N. D. Nguyen, D. H. Bui, and X. T. Tran, “A Novel Hardware Architecture for Human Detection using HOG-SVM Co-Optimization,” Proc. - APCCAS 2019 2019 IEEE Asia Pacific Conf. Circuits Syst. Innov. CAS Towar. Sustain. Energy Technol. Disrupt., pp. 33–36, 2019.
[6] M. Bilal and M. S. Hanif, “Benchmark revision for HOG-SVM pedestrian detector through reinvigorated training and evaluation methodologies,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 3, pp. 1277–1287, 2020.
[7] M. J. Flores Calero, M. Aldas, J. Lazaro, A. Gardel, N. Onofa, and B. Quinga, “Pedestrian detection under partial occlusion by using logic inference, HOG and SVM,” IEEE Lat. Am. Trans., vol. 17, no. 9, pp. 1552–1559, 2019.
[8] F. Utaminingrum et al., “Human guide tracking using combined histogram of oriented gradient and entropy difference minimization algorithm for camera follower,” J. Telecommun. Electron. Comput. Eng., vol. 9, no. 4, pp. 49–54, 2017.
[9] K. P. Divakarla, A. Emadi, and S. Razavi, “A cognitive advanced driver assistance systems architecture for autonomous-capable electrified vehicles,” IEEE Trans. Transp. Electrif., vol. 5, no. 1, pp. 48–58, 2019.
[10] M. Hasenjager, M. Heckmann, and H. Wersing, “A Survey of Personalization for Advanced Driver Assistance Systems,” IEEE Trans. Intell. Veh., vol. 5, no. 2, pp. 335–344, 2020.
[11] J. Chen, M. Bhat, S. Jiang, and D. Zhao, “Advanced driver assistance strategies for a single-vehicle overtaking a platoon on the two-lane two-way road,” IEEE Access, vol. 8, pp. 77285–77297, 2020.
[12] A. Bisoffi, F. Biral, M. Da Lio, and L. Zaccarian, “Longitudinal Jerk Estimation of Driver Intentions for Advanced Driver Assistance Systems,” IEEE/ASME Trans. Mechatronics, vol. 22, no. 4, pp. 1531–1541, 2017.
[13] Z. Sun, G. Bebis, and R. Miller, “On-road vehicle detection: A review,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 5, pp. 694–711, 2006.
[14] L. Mao, M. Xie, Y. Huang, and Y. Zhang, “Preceding vehicle detection using histograms of oriented gradients,” 2010 Int. Conf. Commun. Circuits Syst. ICCCAS 2010 - Proc., pp. 354–358, 2010.
[15] K. R. Skoien, M. O. Alver, and J. A. Alfredsen, “A computer vision approach for detection and quantification of feed particles in marine fish farms,” 2014 IEEE Int. Conf. Image Process. ICIP 2014, pp. 1648–1652, 2014.
[16] X. Cao, C. Wu, P. Yan, and X. Li, “Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos,” in 2011 18th IEEE International Conference on Image Processing, 2011, pp. 2421–2424.
[17] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” Proc. - 2005 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition, CVPR 2005, vol. I, pp. 886–893, 2005.
[18] M. Cheon, W. Lee, C. Yoon, and M. Park, “Vision-Based Vehicle Detection System With Consideration of the Detecting Location,” IEEE Trans. Intell. Transp. Syst., vol. 13, no. 3, pp. 1243–1252, 2012.
[19] Y. Li and G. Su, “Simplified histograms of oriented gradient features extraction algorithm for the hardware implementation,” Proc. - 2015 Int. Conf. Comput. Commun. Syst. ICCCS 2015, pp. 192–195, 2016.
[20] P. A. Raktrakulthum and C. Netramai, “Vehicle classification in congested traffic based on 3D point cloud using SVM and KNN,” 2017 9th Int. Conf. Inf. Technol. Electr. Eng. ICITEE 2017, vol. 2018-Janua, pp. 1–6, 2017.
[21] S. Bougharriou, F. Hamdaoui, and A. Mtibaa, “Linear SVM classifier based HOG car detection,” 2017 18th Int. Conf. Sci. Tech. Autom. Control Comput. Eng. STA 2017 - Proc., vol. 2018-Janua, pp. 241–245, 2018.
DOI: https://doi.org/10.22146/ijccs.54050
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