{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T18:55:36Z","timestamp":1789584936742,"version":"build-2803163510"},"reference-count":30,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T00:00:00Z","timestamp":1728777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U20B2040"],"award-info":[{"award-number":["U20B2040"]}]},{"name":"National Natural Science Foundation of China","award":["62203359"],"award-info":[{"award-number":["62203359"]}]},{"name":"National Natural Science Foundation of China","award":["2022JQ-654"],"award-info":[{"award-number":["2022JQ-654"]}]},{"name":"National Natural Science Foundation of China","award":["3102021ZDHQD10"],"award-info":[{"award-number":["3102021ZDHQD10"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["U20B2040"],"award-info":[{"award-number":["U20B2040"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["62203359"],"award-info":[{"award-number":["62203359"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2022JQ-654"],"award-info":[{"award-number":["2022JQ-654"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["3102021ZDHQD10"],"award-info":[{"award-number":["3102021ZDHQD10"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["U20B2040"],"award-info":[{"award-number":["U20B2040"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["62203359"],"award-info":[{"award-number":["62203359"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2022JQ-654"],"award-info":[{"award-number":["2022JQ-654"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["3102021ZDHQD10"],"award-info":[{"award-number":["3102021ZDHQD10"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The achievement of rapid and reliable image object tracking has long been crucial and challenging for the advancement of image-guided technology. This study investigates real-time object tracking by offering an image target based on nuclear correlation tracking and detection methods to address the challenge of real-time target tracking in complicated environments. In the tracking process, the nuclear-related tracking algorithm can effectively balance the tracking performance and running speed. However, the target tracking process also faces challenges such as model drift, the inability to handle target scale transformation, and target length. In order to propose a solution, this work is organized around the following main points: this study dedicates its first part to the research on kernelized correlation filters (KCFs), encompassing model training, object identification, and a dense sampling strategy based on a circulant matrix. This work developed a scale pyramid searching approach to address the shortcoming that a KCF cannot forecast the target scale. The tracker was expanded in two stages: the first stage output the target\u2019s two-dimensional coordinate location, and the second stage created the scale pyramid to identify the optimal target scale. Experiments show that this approach is capable of resolving the target size variation problem. The second part improved the KCF in two ways to meet the demands of a long-term object tracking task. This article introduces the initial object model, which effectively suppresses model drift. Secondly, an object detection module is implemented, and if the tracking module fails, the algorithm is redirected to the object detection module. The target detection module utilizes two detectors, a variance classifier and a KCF. Finally, this work includes trials on object tracking experiments and subsequent analysis of the results. Initially, this research provides a tracking algorithm assessment system, including an assessment methodology and the collection of test videos, which helped us to determine that the suggested technique outperforms the KCF tracking method. Additionally, the implementation of an evaluation system allows for an objective comparison of the proposed algorithm with other prominent tracking methods. We found that the suggested method outperforms others in terms of its accuracy and resilience.<\/jats:p>","DOI":"10.3390\/s24206600","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T07:58:32Z","timestamp":1729065512000},"page":"6600","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Real-Time Tracking Target System Based on Kernelized Correlation Filter in Complicated Areas"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6555-2563","authenticated-orcid":false,"given":"Abdel Hamid","family":"Mbouombouo Mboungam","sequence":"first","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an 710002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongfeng","family":"Zhi","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an 710002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8138-4103","authenticated-orcid":false,"given":"Cedric Karel","family":"Fonzeu Monguen","sequence":"additional","affiliation":[{"name":"Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology (KIT), Engesserstr. 20, 76131 Karlsruhe, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9637","DOI":"10.1016\/j.aej.2022.02.068","article-title":"A novel real-time multiple objects detection and tracking framework for different challenges","volume":"61","author":"Abdulghafoor","year":"2022","journal-title":"Alex. Eng. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Lui, Y.M., Draper, B.A., and Beveridge, J.R. (2009, January 7\u20139). Simple real-time human detection using a single correlation filter. Proceedings of the IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), Snowbird, UT, USA.","DOI":"10.1109\/PETS-WINTER.2009.5399555"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1561\/0100000006","article-title":"Toeplitz and Circulant Matrices: A Review","volume":"2","author":"Gray","year":"2005","journal-title":"Found. Trends\u00ae Commun. Inf. Theory"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Rauhut, H. (2010). Compressive Sensing and Structured Random Matrices. Theoretical Foundations and Numerical Methods for Sparse Recovery, De Gruyter eBooks.","DOI":"10.1515\/9783110226157.1"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1109\/5.53403","article-title":"Iterative methods for image deblurring","volume":"78","author":"Biemond","year":"1990","journal-title":"Proc. IEEE"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F.S., and Felsberg, M. (2014, January 1\u20135). Accurate Scale Estimation for Robust Visual Tracking. Proceedings of the British Machine Vision Conference, Nottingham, UK.","DOI":"10.5244\/C.28.65"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"012043","DOI":"10.1088\/1742-6596\/2024\/1\/012043","article-title":"Research on Target Tracking Algorithm Based on Correlation Filtering","volume":"2024","author":"Guo","year":"2024","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s00530-022-00996-6","article-title":"DATaR: Depth Augmented Target Redetection using Kernelized Correlation Filter","volume":"29","author":"Yadav","year":"2022","journal-title":"Multimed. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fan, L., Zhang, S., and Qiao, X. (2017). Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters. Sensors, 17.","DOI":"10.3390\/s17112626"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yadav, S., and Payandeh, S. (2021). Critical Overview of Visual Tracking with Kernel Correlation Filter. Technologies, 9.","DOI":"10.3390\/technologies9040093"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8979","DOI":"10.1007\/s11042-017-4790-y","article-title":"Object tracking using distribution fields with correlation coefficients","volume":"77","author":"Qin","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"22","DOI":"10.9766\/KIMST.2023.26.1.022","article-title":"Target Tracking based on Kernelized Correlation Filter Using MWIR and SWIR Sensors","volume":"26","author":"Sun","year":"2023","journal-title":"J. Korea Inst. Mil. Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shi, G., Zhang, B., Geng, X., Liu, Y., and Chai, X. (2022, January 27\u201330). Improvement of Kernel Correlation Filtering Algorithm Based on Kalman Filter. Proceedings of the 2022 IEEE 17th International Conference on Control & Automation (ICCA), Naples, Italy.","DOI":"10.1109\/ICCA54724.2022.9831886"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106250","DOI":"10.1016\/j.engappai.2023.106250","article-title":"A model adaptive updating kernel correlation filter tracker with deep CNN features","volume":"123","author":"Feng","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_15","unstructured":"Chao, M., Xiaokang, Y., Chongyang, Z., and Yang, M.-H. (2015, January 7\u201312). Long-term correlation tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, N., Shi, J., Yeung, D.Y., and Jia, J. (2015, January 7\u201313). Understanding and Diagnosing Visual Tracking Systems. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.355"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","article-title":"High-Speed Tracking with Kernelized Correlation Filters","volume":"37","author":"Henriques","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014, January 6\u201312). Computer Vision-ECCV. Proceedings of the 13th European Conference 2014, Zurich, Switzerland. Proceedings, Part V.","DOI":"10.1007\/978-3-319-10578-9"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Bruce, J.R.B., Draper, A., and Lui, Y.M. (2010, January 13\u201318). Visual object tracking using adaptive correlation filters. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fan, Y., Fei, F., Wang, G., Sun, T., and Niu, L. (2022, January 25\u201327). PSR-Based Feature Adaptation with Anomaly Re-Prediction for Maritime Target Tracking. Proceedings of the 2022 41st Chinese Control Conference (CCC), Hefei, China.","DOI":"10.23919\/CCC55666.2022.9902615"},{"key":"ref_21","unstructured":"Yi, W., Jongwoo, L., and Hsuan, Y.M. (2013, January 23\u201328). Online object tracking: A benchmark. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, OR, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yan, H., Zhang, W., Li, M., and Liu, L. (2023). An adaptive spatiotemporal correlation filtering visual tracking method. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0279240"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1109\/TPAMI.2015.2509974","article-title":"Struck: Structured Output Tracking with Kernels","volume":"38","author":"Hare","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2356","DOI":"10.1109\/TIP.2014.2313227","article-title":"Robust Object Tracking via Sparse Collaborative Appearance Model","volume":"23","author":"Zhong","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kalal, Z., Matas, J., and Mikolajczyk, K. (2010, January 13\u201318). P-N learning: Bootstrapping binary classifiers by structural constraints. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540231"},{"key":"ref_26","unstructured":"Jia, N.X., Lu, N.H., and Yang, N.M. (2012, January 16\u201321). Visual tracking via adaptive structural local sparse appearance model. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_27","unstructured":"Kwon, N.J., and Lee, N.K.M. (2011, January 6\u201313). Tracking by Sampling Trackers. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1109\/TPAMI.2013.32","article-title":"Highly Nonrigid Object Tracking via Patch-Based Dynamic Appearance Modeling","volume":"35","author":"Kwon","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dinh, T.B., Vo, N., and Medioni, G. (2011, January 20\u201325). Context tracker: Exploring supporters and distracters in unconstrained environments. Proceedings of the International Conference on Computer Vision (ICCV), Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995733"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, B., Huang, J., Yang, L., and Kulikowsk, C. (2011, January 20\u201325). Robust tracking using local sparse appearance model and K-selection. Proceedings of the International Conference on Computer Vision (ICCV), Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995730"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/20\/6600\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:12:37Z","timestamp":1760112757000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/20\/6600"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,13]]},"references-count":30,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24206600"],"URL":"https:\/\/doi.org\/10.3390\/s24206600","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,13]]}}}