{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:48:03Z","timestamp":1760240883579,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T00:00:00Z","timestamp":1569456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Foundation of Preliminary Research Field of China","award":["6140312030217","61405170206"],"award-info":[{"award-number":["6140312030217","61405170206"]}]},{"name":"13th Five-Year&quot; Equipment Development Project of China","award":["41412010202"],"award-info":[{"award-number":["41412010202"]}]},{"name":"the Open Foundation of Shaanxi Key Laboratory of Integrated and Intelligent Navigation","award":["SKLIIN-20180108"],"award-info":[{"award-number":["SKLIIN-20180108"]}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972307"],"award-info":[{"award-number":["61972307"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A part-based strategy has been applied to visual tracking with demonstrated success in recent years. Different from most existing part-based methods that only employ one type of tracking representation model, in this paper, we propose an effective complementary tracker based on structural patch response fusion under correlation filter and color histogram models. The proposed method includes two component trackers with complementary merits to adaptively handle illumination variation and deformation. To identify and take full advantage of reliable patches, we present an adaptive hedge algorithm to hedge the responses of patches into a more credible one in each component tracker. In addition, we design different loss metrics of tracked patches in two components to be applied in the proposed hedge algorithm. Finally, we selectively combine the two component trackers at the response maps level with different merging factors according to the confidence of each component tracker. Extensive experimental evaluations on OTB2013, OTB2015, and VOT2016 datasets show outstanding performance of the proposed algorithm contrasted with some state-of-the-art trackers.<\/jats:p>","DOI":"10.3390\/s19194178","type":"journal-article","created":{"date-parts":[[2019,9,27]],"date-time":"2019-09-27T03:03:15Z","timestamp":1569553395000},"page":"4178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Robust Visual Tracking Using Structural Patch Response Map Fusion Based on Complementary Correlation Filter and Color Histogram"],"prefix":"10.3390","volume":"19","author":[{"given":"Zhaohui","family":"Hao","sequence":"first","affiliation":[{"name":"School of Mechano-Electronic Engineering, Xidian University, Xi\u2019an 710071, Shaanxi, China"},{"name":"Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi\u2019an 710068, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guixi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechano-Electronic Engineering, Xidian University, Xi\u2019an 710071, Shaanxi, China"},{"name":"Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi\u2019an 710068, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayu","family":"Gao","sequence":"additional","affiliation":[{"name":"Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi\u2019an 710068, Shaanxi, China"},{"name":"Xi\u2019an Research Institute of Navigation Technology, Xi\u2019an 710068, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechano-Electronic Engineering, Xidian University, Xi\u2019an 710071, Shaanxi, China"},{"name":"Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi\u2019an 710068, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,26]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"A survey of appearance models in visual object tracking","volume":"4","author":"Li","year":"2013","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5867","DOI":"10.1109\/TIP.2016.2615812","article-title":"Visual tracking under motion blur","volume":"25","author":"Ma","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hare, S., Saffari, A., and Torr, P.H.S. (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_4","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ma, S., and Sclaroff, S. (2014, January 8\u201311). MEEM: Robust tracking via multiple experts using entropy minimization. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10599-4_13"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., Draper, B.A., and Lui, Y.M. (2010, January 13\u201318). Visual object tracking using adaptive correlation filters. Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., 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_8","doi-asserted-by":"crossref","unstructured":"Mueller, M., Smith, N., and Ghanem, B. (2017, January 21\u201326). Context-aware correlation filter tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.152"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, F., Tian, C., Zuo, W., Zhang, L., and Yang, M.H. (2018, January 18\u201323). Learning spatial-temporal regularized correlation filters for visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVRP), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00515"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","article-title":"Object tracking benchmark","volume":"37","author":"Wu","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","unstructured":"Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Cehovin, L., Vojir, T., Hager, G., Lukezic, A., and Fernandez, G. (2016, January 8\u201316). The visual object tracking VOT2016 challenge results. Proceedings of the European Conference on Computer Vision Workshops (ECCV), Amsterdam, The Netherlands."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object detection with discriminatively trained part-based models","volume":"32","author":"Felzenszwalb","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., and Torr, P.H. (2016, January 27\u201330). Staple: Complementary learners for real-time tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.156"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jvcir.2018.08.018","article-title":"Robust visual tracking via multi-feature response maps fusion using a collaborative local-global layer visual model","volume":"56","author":"Zhang","year":"2018","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_16","unstructured":"Chaudhuri, K., Freund, Y., and Hsu, D. (2009, January 7\u201310). A parameter-free hedging algorithm. Proceedings of the International Conference on Neural Information Processing Systems (NIPS), Vancouver, BC, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1007\/s11263-018-1076-4","article-title":"Adaptive correlation filters with long-term and short-term memory for object tracking","volume":"126","author":"Ma","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Henriques, J.F., Caseiro, R., Martins, P., and Batista, J. (2012, January 7\u201312). Exploiting the circulant structure of tracking-by-detection with kernels. Proceedings of the European Conference on Computer Vision (ECCV), Firenze, Italy.","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Khan, F.S., Felsberg, M., and Weijer, J.V.D. (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_20","doi-asserted-by":"crossref","first-page":"1512","DOI":"10.1109\/TIP.2009.2019809","article-title":"Learning color names for real-world applications","volume":"18","author":"Weijer","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, 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_22","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zhang, Y., Li, D., and Wang, Z. (2019). Parallel correlation filters for real-time visual tracking. Sensors, 19.","DOI":"10.3390\/s19102362"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, Y., Zhou, W., Shi, L., and Li, D. (2018). Motion-aware correlation filters for online visual tracking. Sensors, 18.","DOI":"10.3390\/s18113937"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/TPAMI.2003.1195991","article-title":"Kernel-based object tracking","volume":"25","author":"Comaniciu","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.jvcir.2015.11.010","article-title":"Fast and robust object tracking via accept-reject color histogram-based method","volume":"34","author":"Abdelali","year":"2016","journal-title":"J. Vis. Commun. Image Rep."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Duffner, S., and Garcia, C. (2013, January 1\u20138). PixelTrack: A fast adaptive algorithm for tracking non-rigid objects. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, NSW, Australia.","DOI":"10.1109\/ICCV.2013.308"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Possegger, H., Mauthner, T., and Bischof, H. (2015, January 7\u201312). In defense of color-based model-free tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298823"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s11263-017-1061-3","article-title":"Discriminative correlation filter tracker with channel and spatial reliability","volume":"126","author":"Lukezic","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"56526","DOI":"10.1109\/ACCESS.2018.2872691","article-title":"Complementary tracking via dual color clustering and spatio-temporal regularized correlation learning","volume":"6","author":"Fan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_30","unstructured":"Nejhum, S.M.S., Ho, J., and Yang, M.H. (2008, January 23\u201328). Visual tracking with histograms and articulating blocks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, T., Jia, K., Xu, C., Ma, Y., and Ahuja, N. (2014, January 23\u201328). Partial occlusion handling for visual tracking via robust part matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.164"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yao, R., Shi, Q., Shen, C., Zhang, Y., and Hengel, A.V.D. (2013, January 23\u201328). Part-based visual tracking with online latent structural learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.306"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, T., Wang, G., and Yang, Q. (2015, January 7\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_34","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhu, J., and Hoi, S.C.H. (2015, January 7\u201312). Reliable patch trackers: Robust visual tracking by exploiting reliable patches. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298632"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sun, X., Cheung, N.M., Yao, H., and Guo, Y. (2017, January 22\u201329). Non-rigid object tracking via deformable patches using shape-preserved KCF and level sets. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.586"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.patcog.2018.04.011","article-title":"Robust occlusion-aware part-based visual tracking with object scale adaptation","volume":"81","author":"Wang","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1109\/TCSVT.2016.2539860","article-title":"Robust visual tracking via basis matching","volume":"27","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.patrec.2015.04.010","article-title":"Robust object tracking using semi-supervised appearance dictionary learning","volume":"62","author":"Zhang","year":"2015","journal-title":"Pattern Recognit. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1109\/TNNLS.2016.2586194","article-title":"A biologically inspired appearance model for robust visual tracking","volume":"28","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ma, C., Huang, J.B., Yang, X., and Yang, M.H. (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_41","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1109\/TPAMI.2018.2828817","article-title":"Hedging deep features for visual tracking","volume":"41","author":"Qi","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1109\/TITS.2017.2766093","article-title":"Point-to-set distance metric learning on deep representations for visual tracking","volume":"19","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Khan, F.S., and Felsberg, M. (2019, January 16\u201320). Atom: Accurate tracking by overlap maximization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00479"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.sigpro.2014.08.027","article-title":"Adaptive NormalHedge for robust visual tracking","volume":"110","author":"Zhang","year":"2015","journal-title":"Signal Process."},{"key":"ref_45","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_46","first-page":"551","article-title":"Online passive-aggressive algorithms","volume":"7","author":"Crammer","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_47","unstructured":"Li, Y., and Zhu, J. (2014, January 6\u201312). A scale adaptive kernel correlation filter tracker with feature integration. Proceedings of the European Conference on Computer Vision Workshops (ECCV), Zurich, Switzerland."},{"key":"ref_48","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"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4178\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:24:41Z","timestamp":1760189081000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,26]]},"references-count":48,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19194178"],"URL":"https:\/\/doi.org\/10.3390\/s19194178","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,9,26]]}}}