A Real-Time Application of Singular Spectrum Analysis to Object Tracking with SIFT
Received: 27 April 2022 | Revised: 31 May 2022 | Accepted: 1 June 2022 | Online: 7 August 2022
Corresponding author: A. Ozturk
Abstract
This study combined SIFT and SSA to propose a novel algorithm for real-time object tracking. The proposed algorithm utilizes an intermediate fixed-size buffer and a modified SSA algorithm. Since the complete reconstruction step of the SSA algorithm was unnecessary, it was considerably simplified. In addition, the execution time of a Matlab implementation of the SSA algorithm was compared with a respective C++ implementation. Moreover, the performance of the two different matching algorithms in the detection, the FlannBasedMatcher and Brute-Force matcher algorithms of the OpenCV library, was compared.
Keywords:
Object Tracking, Object Detection, Computer Vision, SIFT, SSADownloads
References
M. Y. Abbass, K.-C. Kwon, N. Kim, S. A. Abdelwahab, F. E. A. El-Samie, and A. A. M. Khalaf, "A survey on online learning for visual tracking," The Visual Computer, vol. 37, no. 5, pp. 993–1014, May 2021. DOI: https://doi.org/10.1007/s00371-020-01848-y
M. Haris et al., "Recognition and Tracking of Objects in a Clustered Remote Scene Environment," Computers, Materials & Continua, vol. 70, no. 1, Sep. 2021, Art. no. 1699. DOI: https://doi.org/10.32604/cmc.2022.019572
Z. Machkour, D. Ortiz-Arroyo, and P. Durdevic, "Classical and Deep Learning based Visual Servoing Systems: a Survey on State of the Art," Journal of Intelligent & Robotic Systems, vol. 104, no. 1, Dec. 2021, Art. no. 11. DOI: https://doi.org/10.1007/s10846-021-01540-w
J. Zuo, Z. Jia, J. Yang, and N. Kasabov, "Moving Target Detection Based on Improved Gaussian Mixture Background Subtraction in Video Images," IEEE Access, vol. 7, pp. 152612–152623, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2946230
A. Yilmaz, O. Javed, and M. Shah, "Object tracking: A survey," ACM Computing Surveys, vol. 38, no. 4, Sep. 2006, Art. no. 13-es. DOI: https://doi.org/10.1145/1177352.1177355
D. G. Lowe, "Object recognition from local scale-invariant features," in Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, Sep. 1999, vol. 2, pp. 1150–1157. DOI: https://doi.org/10.1109/ICCV.1999.790410
D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, Nov. 2004. DOI: https://doi.org/10.1023/B:VISI.0000029664.99615.94
I. Rey Otero, "Anatomy of the SIFT Method," Image Processing On Line, vol. 4, pp. 370–396, Dec. 2014. DOI: https://doi.org/10.5201/ipol.2014.82
H. Alamri, E. Alshanbari, S. Alotaibi, and M. Alghamdi, "Face Recognition and Gender Detection Using SIFT Feature Extraction, LBPH, and SVM," Engineering, Technology & Applied Science Research, vol. 12, no. 2, pp. 8296–8299, Apr. 2022. DOI: https://doi.org/10.48084/etasr.4735
P. Matlani and M. Shrivastava, "An Efficient Algorithm Proposed For Smoke Detection in Video Using Hybrid Feature Selection Techniques," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 3939–3944, Apr. 2019. DOI: https://doi.org/10.48084/etasr.2571
H. Zhou, Y. Yuan, and C. Shi, "Object tracking using SIFT features and mean shift," Computer Vision and Image Understanding, vol. 113, no. 3, pp. 345–352, Mar. 2009. DOI: https://doi.org/10.1016/j.cviu.2008.08.006
F. Jabar, S. Farokhi, and U. U. Sheikh, "Object tracking using SIFT and KLT tracker for UAV-based applications," in 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), Langkawi, Malaysia, Jul. 2015, pp. 65–68. DOI: https://doi.org/10.1109/IRIS.2015.7451588
B. D. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision," in Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2, San Francisco, CA, USA, May 1981, pp. 674–679.
P. More and P. Mishra, "Enhanced-PCA based Dimensionality Reduction and Feature Selection for Real-Time Network Threat Detection," Engineering, Technology & Applied Science Research, vol. 10, no. 5, pp. 6270–6275, Oct. 2020. DOI: https://doi.org/10.48084/etasr.3801
N. Golyandina, "Particularities and commonalities of singular spectrum analysis as a method of time series analysis and signal processing," WIREs Computational Statistics, vol. 12, no. 4, 2020, Art. no. e1487. DOI: https://doi.org/10.1002/wics.1487
F. J. Alonso, J. M. D. Castillo, and P. Pintado, "Application of singular spectrum analysis to the smoothing of raw kinematic signals," Journal of Biomechanics, vol. 38, no. 5, pp. 1085–1092, May 2005. DOI: https://doi.org/10.1016/j.jbiomech.2004.05.031
A. Ozturk, A. Tartar, B. Ersoz Huseyinsinoglu, and A. H. Ertas, "A clinically feasible kinematic assessment method of upper extremity motor function impairment after stroke," Measurement, vol. 80, pp. 207–216, Feb. 2016. DOI: https://doi.org/10.1016/j.measurement.2015.11.026
K. Ansari, "Real-Time Positioning Based on Kalman Filter and Implication of Singular Spectrum Analysis," IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 1, pp. 58–61, Jan. 2021. DOI: https://doi.org/10.1109/LGRS.2020.2964300
B. Bhowmik, M. Krishnan, B. Hazra, and V. Pakrashi, "Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis," Structural Health Monitoring, vol. 18, no. 2, pp. 563–589, Mar. 2019. DOI: https://doi.org/10.1177/1475921718760483
T. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors: A Survey. Hanover, MA, USA: Now Publishers Inc, 2008. DOI: https://doi.org/10.1561/9781601981394
T. Lindeberg, "On the Axiomatic Foundations of Linear Scale-Space," in Gaussian Scale-Space Theory, J. Sporring, M. Nielsen, L. Florack, and P. Johansen, Eds. Dordrecht: Springer Netherlands, 1997, pp. 75–97. DOI: https://doi.org/10.1007/978-94-015-8802-7_6
I. R. Otero and M. Delbracio, "Computing an Exact Gaussian Scale-Space," Image Processing On Line, vol. 6, pp. 8–26, Feb. 2016. DOI: https://doi.org/10.5201/ipol.2016.117
N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA, Jun. 2005, vol. 1, pp. 886–893 vol. 1.
M. Muja and D. G. Lowe, "Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration," presented at the International Conference on Computer Vision Theory and Applications, Feb. 2009, vol. 1, pp. 331–340.
"Eigen Library," Eigen. https://eigen.tuxfamily.org.
A. Ozturk, "ozturk-ali/SSA," May 30, 2022. https://github.com/ozturk-ali/SSA.
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