{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T10:44:15Z","timestamp":1766486655911,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,14]],"date-time":"2020-06-14T00:00:00Z","timestamp":1592092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31371055"],"award-info":[{"award-number":["31371055"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Visual tracking is a fundamental vision task that tries to figure out instances of several object classes from videos and images. It has attracted much attention for providing the basic semantic information for numerous applications. Over the past 10 years, visual tracking has made a great progress, but huge challenges still exist in many real-world applications. The facade of a target can be transformed significantly by pose changing, occlusion, and sudden movement, which possibly leads to a sudden target loss. This paper builds a hybrid tracker combining the deep feature method and correlation filter to solve this challenge, and verifies its powerful characteristics. Specifically, an effective visual tracking method is proposed to address the problem of low tracking accuracy due to the limitations of traditional artificial feature models, then rich hiearchical features of Convolutional Neural Networks are used to make the multi-layer features fusion improve the tracker learning accuracy. Finally, a large number of experiments are conducted on benchmark data sets OBT-100 and OBT-50, and show that our proposed algorithm is effective.<\/jats:p>","DOI":"10.3390\/s20123370","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T05:56:27Z","timestamp":1592200587000},"page":"3370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Visual Tracking via Deep Feature Fusion and Correlation Filters"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0640-5888","authenticated-orcid":false,"given":"Haoran","family":"Xia","sequence":"first","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing 400715, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9405-3290","authenticated-orcid":false,"given":"Yuanping","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing 400715, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8901-5089","authenticated-orcid":false,"given":"Ming","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing 400715, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6185-5614","authenticated-orcid":false,"given":"Yufang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Faculty of Psychology, Southwest University, Chongqing 400715, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,14]]},"reference":[{"key":"ref_1","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 2015, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299124"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tang, Y., Fang, B., and Shang, Z. (2017, January 15\u201317). Fast multi-object tracking using convolutional neural networks with tracklets updating. Proceedings of the International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017, Shenzhen, China.","DOI":"10.1109\/SPAC.2017.8304296"},{"key":"ref_3","unstructured":"Shu, G., Dehghan, A., Oreifej, O., Hand, E., and Shah, M. (2012, January 16\u201321). Part-based multiple-person tracking with partial occlusion handling. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ledig, C., Heckemann, R.A., Aljabar, P., Wolz, R., Hajnal, J.V., Hammers, A., and Rueckert, D. (2012, January 2\u20135). Multi-class brain segmentation using atlas propagation and EM-based refinement. Proceedings of the 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain.","DOI":"10.1109\/ISBI.2012.6235693"},{"key":"ref_5","first-page":"43:1","article-title":"Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends","volume":"52","author":"Fiaz","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1145\/1177352.1177355","article-title":"Object tracking: A survey","volume":"38","author":"Javed","year":"2006","journal-title":"ACM Comput. Surv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/TPAMI.2007.35","article-title":"Ensemble Tracking","volume":"29","author":"Avidan","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bai, Q., Wu, Z., Sclaroff, S., Betke, M., and Monnier, C. (2013, January 1\u20138). Randomized Ensemble Tracking. Proceedings of the IEEE International Conference on Computer Vision, ICCV 2013, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.255"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Girshick, R.B., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_10","unstructured":"Wang, N., and Yeung, D. (2013, January 5\u20138). Learning a Deep Compact Image Representation for Visual Tracking. Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013, Lake Tahoe, NV, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1109\/TIP.2015.2403231","article-title":"Video Tracking Using Learned Hierarchical Features","volume":"24","author":"Wang","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, H., Li, Y., and Porikli, F. (2014, January 1\u20135). DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking. Proceedings of the British Machine Vision Conference (BMVC 2014), Nottingham, UK.","DOI":"10.5244\/C.28.56"},{"key":"ref_13","unstructured":"Hong, S., You, T., Kwak, S., and Han, B. (2015, January 6\u201311). Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_14","unstructured":"Lucas, B.D., and Kanade, T. (1981, January 24\u201328). An Iterative Image Registration Technique with an Application to Stereo Vision. Proceedings of the 7th International Joint Conference on Artificial Intelligence, Vancouver, BC, Canada."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Ma, C., Huang, J., Yang, X., and Yang, M. (2015, January 7\u201313). Hierarchical Convolutional Features for Visual Tracking. Proceedings of the 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.352"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s00138-010-0314-8","article-title":"A fuzzy inference approach to template-based visual tracking","volume":"23","year":"2012","journal-title":"Mach. Vis. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1016\/j.imavis.2006.04.008","article-title":"Novel target segmentation and tracking based on fuzzy membership distribution for vision-based target tracking system","volume":"24","author":"Kim","year":"2006","journal-title":"Image Vis. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.neucom.2017.11.060","article-title":"Fuzzy logic approach to visual multi-object tracking","volume":"281","author":"Li","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_20","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."},{"key":"ref_21","first-page":"234","article-title":"Semi-supervised On-Line Boosting for Robust Tracking","volume":"Volume 5302","author":"Forsyth","year":"2008","journal-title":"Proceedings of the 10th European Conference on Computer Vision"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1007\/978-3-319-10578-9_13","article-title":"Transfer Learning Based Visual Tracking with Gaussian Processes Regression","volume":"Volume 8691","author":"Fleet","year":"2014","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2014\u201413th European Conference"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1109\/TPAMI.2011.239","article-title":"Tracking-Learning-Detection","volume":"34","author":"Kalal","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1007\/978-3-319-10599-4_13","article-title":"MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization","volume":"Volume 8694","author":"Fleet","year":"2014","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2014\u201413th European Conference"},{"key":"ref_25","unstructured":"Metaxas, D.N., Quan, L., Sanfeliu, A., and Gool, L.V. (2011, January 6\u201313). Struck: Structured output tracking with kernels. Proceedings of the IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1111\/cgf.13080","article-title":"Building a Large Database of Facial Movements for Deformation Model-Based 3D Face Tracking","volume":"36","author":"Sibbing","year":"2017","journal-title":"Comput. Graph. Forum"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1109\/TPAMI.2008.299","article-title":"Differential Earth Mover\u2019s Distance with Its Applications to Visual Tracking","volume":"32","author":"Zhao","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","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 Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1007\/978-3-642-33765-9_50","article-title":"Exploiting the Circulant Structure of Tracking-by-Detection with Kernels","volume":"Volume 7575","author":"Fitzgibbon","year":"2012","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2012\u201412th European Conference on Computer Vision"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Khan, F.S., Felsberg, M., and van de Weijer, J. (2014, January 23\u201328). Adaptive Color Attributes for Real-Time Visual Tracking. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.143"},{"key":"ref_33","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 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00479"},{"key":"ref_34","unstructured":"Metaxas, D.N., Quan, L., Sanfeliu, A., and Gool, L.V. (2011, January 6\u201313). Superpixel tracking. Proceedings of the IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ruan, Y., and Wei, Z. (2016). Real-Time Visual Tracking through Fusion Features. Sensors, 16.","DOI":"10.3390\/s16070949"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2709","DOI":"10.1109\/TPAMI.2018.2865311","article-title":"Robust Visual Tracking via Hierarchical Convolutional Features","volume":"41","author":"Ma","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201326). Histograms of Oriented Gradients for Human Detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1007\/978-3-319-10602-1_26","article-title":"Edge Boxes: Locating Object Proposals from Edges","volume":"Volume 8693","author":"Fleet","year":"2014","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2014\u201413th European Conference"},{"key":"ref_39","unstructured":"Metaxas, D.N., Quan, L., Sanfeliu, A., and Gool, L.V. (2011, January 6\u201313). Graph mode-based contextual kernels for robust SVM tracking. Proceedings of the IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain."},{"key":"ref_40","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1109\/TPAMI.2008.194","article-title":"Tracking by Affine Kernel Transformations Using Color and Boundary Cues","volume":"31","author":"Leichter","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","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 (BMVC 2014), Nottingham, UK.","DOI":"10.5244\/C.28.65"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Galoogahi, H.K., Sim, T., and Lucey, S. (2013, January 1\u20138). Multi-channel Correlation Filters. Proceedings of the IEEE International Conference on Computer Vision, ICCV 2013, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.381"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Boddeti, V.N., Kanade, T., and Kumar, B.V.K.V. (2013, January 23\u201328). Correlation Filters for Object Alignment. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.297"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chang-zhen, X., Man-qiang, C., Run-ling, W., and Yan, L. (2018). Adaptive Model Update via Fusing Peak-to-sidelobe Ratio and Mean Frame Difference for Visual Tracking. Acta Photonica Sin., 47.","DOI":"10.3788\/gzxb20184709.0910001"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Li, F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lim, J., and Yang, M. (2013, January 23\u201328). Online Object Tracking: A Benchmark. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.312"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., and Lenc, K. (2014). MatConvNet\u2014Convolutional Neural Networks for MATLAB. CoRR.","DOI":"10.1145\/2733373.2807412"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/978-3-319-10602-1_9","article-title":"Fast Visual Tracking via Dense Spatio-temporal Context Learning","volume":"Volume 8693","author":"Fleet","year":"2014","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2014\u201413th European Conference"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1007\/978-3-642-33712-3_62","article-title":"Real-Time Compressive Tracking","volume":"Volume 7574","author":"Fitzgibbon","year":"2012","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2012\u201412th European Conference on Computer Vision"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"He, S., Yang, Q., Lau, R.W.H., Wang, J., and Yang, M. (2013, January 23\u201328). Visual Tracking via Locality Sensitive Histograms. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.314"},{"key":"ref_52","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_53","first-page":"254","article-title":"A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration","volume":"Volume 8926","author":"Agapito","year":"2014","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2014 Workshops"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1109\/TPAMI.2016.2609928","article-title":"Discriminative Scale Space Tracking","volume":"39","author":"Danelljan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_55","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, Lake Tahoe, NV, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3370\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:38:51Z","timestamp":1760175531000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3370"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,14]]},"references-count":55,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20123370"],"URL":"https:\/\/doi.org\/10.3390\/s20123370","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,6,14]]}}}