{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T01:09:14Z","timestamp":1760404154240,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,15]],"date-time":"2017-11-15T00:00:00Z","timestamp":1510704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["61671365","61372091"],"award-info":[{"award-number":["61671365","61372091"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2017JM6018"],"award-info":[{"award-number":["2017JM6018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, visual object tracking has been widely used in military guidance, human-computer interaction, road traffic, scene monitoring and many other fields. The tracking algorithms based on correlation filters have shown good performance in terms of accuracy and tracking speed. However, their performance is not satisfactory in scenes with scale variation, deformation, and occlusion. In this paper, we propose a scene-aware adaptive updating mechanism for visual tracking via a kernel correlation filter (KCF). First, a low complexity scale estimation method is presented, in which the corresponding weight in five scales is employed to determine the final target scale. Then, the adaptive updating mechanism is presented based on the scene-classification. We classify the video scenes as four categories by video content analysis. According to the target scene, we exploit the adaptive updating mechanism to update the kernel correlation filter to improve the robustness of the tracker, especially in scenes with scale variation, deformation, and occlusion. We evaluate our tracker on the CVPR2013 benchmark. The experimental results obtained with the proposed algorithm are improved by 33.3%, 15%, 6%, 21.9% and 19.8% compared to those of the KCF tracker on the scene with scale variation, partial or long-time large-area occlusion, deformation, fast motion and out-of-view.<\/jats:p>","DOI":"10.3390\/s17112626","type":"journal-article","created":{"date-parts":[[2017,11,15]],"date-time":"2017-11-15T11:13:35Z","timestamp":1510744415000},"page":"2626","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7566-1634","authenticated-orcid":false,"given":"Fan","family":"Li","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, School of Electronic and Information Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sirou","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, School of Electronic and Information Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoya","family":"Qiao","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, School of Electronic and Information Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1145\/1177352.1177355","article-title":"Object tracking: A survey","volume":"38","author":"Yilmaz","year":"2006","journal-title":"ACM Comput. Surv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lee, D.Y., Sim, J.Y., and Kim, C.S. (2014, January 23\u201328). Visual Tracking Using Pertinent Patch Selection and Masking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.446"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1109\/TPAMI.2003.1233903","article-title":"Robust Online Appearance Models for Visual Tracking","volume":"25","author":"Jepson","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Grabner, H., Grabner, M., and Bischof, H. (2006, January 4\u20137). Real-Time Tracking via On-line Boosting. Proceedings of the British Machine Vision Conference, Edinburgh, UK.","DOI":"10.5244\/C.20.6"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11263-007-0075-7","article-title":"Incremental Learning for Robust Visual Tracking","volume":"77","author":"Ross","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_6","first-page":"2356","article-title":"Robust Object Tracking via Sparse Collaborative Appearance Model","volume":"23","author":"Zhong","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","unstructured":"Ji, H. (2012, January 16\u201321). Real time robust L1 tracker using accelerated proximal gradient approach. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lim, J., and Yang, M.H. (2013, January 23\u201328). Online Object Tracking: A Benchmark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.312"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kwon, J., and Lee, K.M. (2010, January 13\u201318). Visual tracking decomposition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539821"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2002","DOI":"10.1109\/TPAMI.2014.2315808","article-title":"Fast Compressive Tracking","volume":"36","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","unstructured":"Lu, H., Jia, X., and Yang, M.H. (2012, January 16\u201321). Visual tracking via adaptive structural local sparse appearance model. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA."},{"key":"ref_12","unstructured":"Jepson, A.D., Fleet, D.J., and Elmaraghi, T.F. (2001, January 8\u201314). Robust Online Appearance Models for Visual Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, HI, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1109\/TPAMI.2005.205","article-title":"Online selection of discriminative tracking features","volume":"27","author":"Collins","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.patcog.2012.07.013","article-title":"Real-time visual tracking via online weighted multiple instance learning","volume":"46","author":"Zhang","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Henriques, J.F., Rui, C., and Martins, P. (2012, January 7\u201313). Exploiting the Circulant Structure of Tracking-by-Detection with Kernels. Proceedings of the European Conference on Computer Vision (ECCV), Florence, Italy.","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1109\/TPAMI.2004.16","article-title":"The template update problem","volume":"26","author":"Matthews","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.knosys.2016.09.014","article-title":"A multi-view model for visual tracking via correlation filters","volume":"113","author":"Li","year":"2016","journal-title":"Knowl.-Based Sys."},{"key":"ref_18","first-page":"4199","article-title":"Generalized Pooling for Robust Object Tracking","volume":"25","author":"Ma","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","first-page":"5867","article-title":"Visual Tracking under Motion Blur","volume":"25","author":"Ma","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Gustav, H., Khan, F.S., and Felsberg, M. (2015, January 7\u201313). Learning Spatially Regularized Correlation Filters for Visual Tracking. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.490"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.patcog.2017.04.004","article-title":"Robust Visual Tracking via Co-trained Kernelized Correlation Filters","volume":"69","author":"Zhang","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3004","DOI":"10.1016\/j.patcog.2015.02.003","article-title":"Exemplar based Deep Discriminative and Shareable Feature Learning for Scene Image Classification","volume":"48","author":"Zuo","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., and Draper, B.A. (2010, January 13\u201318). Visual object tracking using adaptive correlation filters. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., and Khan, F.S. (2014, January 1\u20135). Accurate Scale Estimation for Robust Visual Tracking. Proceedings of the British Machine Vision Conference (BMVC), Nottingham, UK.","DOI":"10.5244\/C.28.65"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Ma, C., Yang, X., and Zhang, C. (2015, January 8\u201312). Long-term correlation tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299177"},{"key":"ref_27","first-page":"146","article-title":"Discriminative descriptors for object tracking","volume":"35","author":"Yang","year":"2016","journal-title":"Knowl.-Based Sys."},{"key":"ref_28","unstructured":"Dalal, N., and Triggs, B. (2005, January 21\u201323). Histograms of Oriented Gradients for Human Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Khan, F.S., and Felsberg, M. (2014, January 23\u201328). Adaptive Color Attributes for Real-Time Visual Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.143"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, T., Wang, G., and Yang, Q. (2015, January 8\u201312). Real-time part-based visual tracking via adaptive correlation filters. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299124"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jeong, S., Kim, G., and Lee, S. (2017). Effective Visual Tracking Using Multi-Block and Scale Space Based on Kernelized Correlation Filters. Sensors, 17.","DOI":"10.3390\/s17030433"},{"key":"ref_32","unstructured":"Wang, C., Zhang, L., and Xie, L. (2017, November 14). Kernel Cross-Correlator. Available online: https:\/\/arxiv.org\/pdf\/1709.05936.pdf."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ma, C., Huang, J.B., and Yang, X. (2015, January 7\u201313). Hierarchical Convolutional Features for Visual Tracking. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.352"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hare, S., Torr, P., and Saffari, A. (2011, January 6\u201313). Struck: Structured Output Tracking with Kernels. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126251"},{"key":"ref_35","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540231"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1109\/TPAMI.2010.226","article-title":"Robust Object Tracking with Online Multiple Instance Learning","volume":"33","author":"Babenko","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/11\/2626\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:49:41Z","timestamp":1760208581000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/11\/2626"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,15]]},"references-count":36,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2017,11]]}},"alternative-id":["s17112626"],"URL":"https:\/\/doi.org\/10.3390\/s17112626","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2017,11,15]]}}}