HUMAN TRACKING OBJECTS IN DARK SITUATIONS BASED ON THERMAL USING SUPPORT VECTOR MACHINES, L1 TRACKER USING ACCELERATED PROXIMAL GRADIENT APPROACH, AND KERNELIZED CORRELATION FILTER METHODS
DOI:
https://doi.org/10.25124/cepat.v1i03.5268Keywords:
Kernelized Correlation Filter, L1 Tracker Using Accelerated Proximal Gradient Approach, Success Plot, Support Vector Machines, Object Tracking, Precision PlotAbstract
Pedestrian safety on pedestrian lanes on the side of highways or roads in housing with heavy or quiet traffic conditions needs to be a public concern. Security that must be considered, object tracking is needed to carry out surveillance in improving pedestrian security, and it is also necessary to install thermal camera devices to find out the position of objects such as humans, in various positions of viewpoints that can be applied and applied to monitor the environment. To classify objects such as humans who are in dark or low-light conditions, namely by using the Kernelized Correlation Filter (KCF) tracking method, Support Vector Machines (SVM), and L1 Tracker Using Accelerated Proximal Gradient Approach (L1APG) based on a distance of 10 meters, 15 meters, 20 meters and the size of the object in the dataset. The results of the study with 1684 image inputs. Good performance for each success plot distance on the SVM method is 99.25%, 99.75%, 98.74% because it can track successfully based on the object being traced. Good performance for each precision plot distance on the KCF method of 51.88%, 46.8%, 63.81% has precise accuracy results against the object being tracked.
Downloads
References
Yilmaz. A, Javed. O, dan Shah. M, (2006). “Object Tracking: A Survey, ACM Computing Surveys”. Vol. 38 No. 4, Article 13, doi: 10.1145/1177352.1177355
Q. Liu, Z. He, X. Li, and Y. Zheng, “PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark,” IEEE Trans. Multimed., vol. 22, no. 3, pp. 666–675, 2020, doi: 10.1109/TMM.2019.2932615.
Dompeipen. T. A, Sompie. S.R.U.A, dan Najoan. M.E.I, “Computer Vision Implementation for Detection and Counting the Number of Humans,” Jurusan Teknik Elektro, Universitas Sam Ratulangi Manado, Jurnal Teknik Informatika vol. 16 no. 1 Januari-Maret 2021, hal. 65-76 p-ISSN : 2301-8364, e-ISSN : 2685-6131
H. Al Kautsar and K. Adi, “Implementasi Object Tracking untuk Mendeteksi dan Menghitung Jumlah Kendaraan Secara Otomatis Menggunakan Metode Kalman Filter dan Gaussian Mixture Model,” Youngster Phys. J., vol. 5, no. 1, pp.13-20,2016
D.A. Prabowo, D. Abdullah, A. Manik., “Deteksi dan Perhitungan Objek Berdasarkan Warna Menggunakan Color Object Tracking,” jurnal Pseudocode, Volume V Nomor 2, September 2018, ISSN 2355-5920 www.ejournal.unib.ac.id/index.php/pseudocode 85
B. Shilei, T. Xiujun, Z. Jiaxin, “Research on Object Tracking Algorithm Based on KCF,” 2020, International Conference on Culture-oriented Science & Technology (ICCST)
W. Qiang, Z. Zhou, “Long- term Tracking Based on Kernelized Correlation Filtering,” 2018, International Conference on Network and Information Systems for Computers
H. Tao, X. Shen, Q. Deng, “Infrared Target Tracking Algorithm Based on Bernaolli Filter and Support Vector Machine,” 2020, International Conference on Information Science and Education (ICISE-IE)
Supreeth, H. S. G, and C. M. Patil, “An Adaptive SVM Technique for Object Tracking,” International Journal of Pure and Applied Mathematics, Volume 118 No. 7 2018, 131-135, ISSN: 1314-3395
H. Song, Mei-li Shen, “A Specific Target Track Method Based on SVM and AdaBoost,” 2008, International Symposium on Computer Science and Computational Technology
Z. Shen, K. Toh, and S. Yun, “An Accelerated Proximal Gradient Algorithm for Frame-Based Image Restorations via the Balanced Approach,” 2011, SIAM J on Imag. Sci, doi: 10.1137/090779437
C. Bao, Y. Wiu, H. Ling, and H. Ji, “Real Time Robust L1 Tracker Using Accelerated Proximal Gradient Approach,” 2012, IEEE, doi: 10.1109/CVPR.2012.6247881
Shan. Jiang, Xiaoqiang. DI, “An Efficient Misalignment Method for Visual Tracking Based on Sparse Representation,” IEICE TRANS. INF. & SYST., VOL.E101–D, NO.8 AUGUST 2018
Y. Wu, J. Lim, M. Yang, “Online Object Tracking: A Benchmark,” 2013, IEEE Conference on Computer Vision and Pattern Recognition, doi: 10.1109/CVPR.2013.312
J. Park, S. Kim, and Y. Lee, “Improvement of the KCF Tracking Algorithm through Object Detection,” 2012, International Journal of Engineering & Technology
F. Joao, Henriques, R. Caseiro, P. Martins, and J. Batista, “High-Speed Tracking with Kernelized Correlation Filters,” 2015, IEEE Transactions On Pattern Analysis And Machine Intelligence, doi: 10.1109/TPAMI.2014.2345390
Rajasekhar Nannapaneni, “Machine Learning-Based Object Tracking,” 2019, Sr Principal Engineer, Solutions Architect, Dell Technologies
J. S. Kulchandani, K. J. Dangarwala, “Moving Object Detection: Review of Recent Research Trends,” 2015, International Conference on Pervasive Computing (ICPC), doi: 10.1109/PERVASIVE.2015.7087138
M. A. H. Baso, “Peningkatan Performansi Kernel-Based Object Tracking Menggunakan Type-2 Fuzzy Logic,” 2019, Universitas Telkom, Bandung
Downloads
Published
Issue
Section
License
CEPAT has chosen to apply the Creative Commons Attribution NonCommercial 4.0 License (CC BY-NC 4.0) to all manuscripts to be published. Authors who publish with this journal agree to the following terms.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
