{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:00:24Z","timestamp":1760241624924,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,8]],"date-time":"2018-06-08T00:00:00Z","timestamp":1528416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nature Science Foundation of China","award":["61672335, 61602191"],"award-info":[{"award-number":["61672335, 61602191"]}]},{"name":"Foundation of Fujian Education Department","award":["JAT170053"],"award-info":[{"award-number":["JAT170053"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>To boost the robustness of the traditional particle-filter-based tracking algorithm under complex scenes and to tackle the drift problem that is caused by the fast moving target, an improved particle-filter-based tracking algorithm is proposed. Firstly, all of the particles are divided into two parts and put separately. The number of particles that are put for the first time is large enough to ensure that the number of the particles that can cover the target is as many as possible, and then the second part of the particles are put at the location of the particle with the highest similarity to the template in the particles that are first put, to improve the tracking accuracy. Secondly, in order to obtain a sparser solution, a novel minimization model for an Lp tracker is proposed. Finally, an adaptive multi-feature fusion strategy is proposed, to deal with more complex scenes. The experimental results demonstrate that the proposed algorithm can not only improve the tracking robustness, but can also enhance the tracking accuracy in the case of complex scenes. In addition, our tracker can get better accuracy and robustness than several state-of-the-art trackers.<\/jats:p>","DOI":"10.3390\/info9060140","type":"journal-article","created":{"date-parts":[[2018,6,8]],"date-time":"2018-06-08T11:19:31Z","timestamp":1528456771000},"page":"140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Target Tracking Algorithm Based on an Adaptive Feature and Particle Filter"],"prefix":"10.3390","volume":"9","author":[{"given":"Yanming","family":"Lin","sequence":"first","affiliation":[{"name":"College of Engineering, Huaqiao University, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8542-3728","authenticated-orcid":false,"given":"Detian","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Engineering, Huaqiao University, Quanzhou 362021, China"},{"name":"University Engineering Research Center of Fujian Province Industrial Intelligent Technology and Systems, Huaqiao University, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqin","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Engineering, Huaqiao University, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"81","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","first-page":"1442","DOI":"10.1109\/TPAMI.2013.230","article-title":"Visual Tracking: An Experimental Survey","volume":"36","author":"Smeulders","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","unstructured":"Che, Z., Hong, Z., and Tao, D. (2005, January 20\u201325). An Experimental Survey on Correlation Filter-based Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, H., Shen, C., and Shi, Q. (2011, January 20\u201325). Real-time visual tracking using compressive sensing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2011.5995483"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2259","DOI":"10.1109\/TPAMI.2011.66","article-title":"Robust visual tracking and vehicle classification via sparse representation","volume":"33","author":"Mei","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","unstructured":"Mei, X., and Ling, H.B. (October, January 29). Robust Visual Tracking Using L1 Minimization. Proceedings of the IEEE International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_7","unstructured":"Mei, X., Ling, H.B., Wu, Y., Blasch, E., and Bai, L. (2011, January 20\u201325). Minimum error bounded efficient L1 tracker with occlusion detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_8","unstructured":"Bao, C.L., Wu, Y., Ling, H.B., and 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, Providence, RI, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11263-014-0738-0","article-title":"Robust visual tracking via consistent low-rank sparse learning","volume":"111","author":"Zhang","year":"2014","journal-title":"Int. J. Comput. Vis."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, T., Ghanem, B., Liu, S., and Ahuja, N. (2012, January 7\u201313). Low-rank sparse learning for robust visual tracking. Proceedings of the European Conference on Computer Vision, Florence, Italy.","DOI":"10.1007\/978-3-642-33783-3_34"},{"key":"ref_11","first-page":"485","article-title":"Temporal Restricted Visual Tracking via Reverse-Low-Rank Sparse Learning","volume":"47","author":"Yang","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_12","unstructured":"Chartrand, R. (July, January 28). Fast algorithms for nonconvex compressive sensing: MRI reconstruction from very few data. Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zuo, W., Meng, D., Zhang, L., Feng, X., and Zhang, D. (2013, January 1\u20138). A generalized iterated shrinkage algorithm for non-convex sparse coding. Proceedings of the IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","DOI":"10.1109\/ICCV.2013.34"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chartrand, R. (2014, January 4\u20139). Shrinkage mappings and their induced penalty functions. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal, Florence, Italy.","DOI":"10.1109\/ICASSP.2014.6853752"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hong, Z., Mei, X., Prokhorov, D., and Tao, D. (2013, January 1\u20138). Tracking via Robust Multi-task Multi-view Joint Sparse Representation. Proceedings of the IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","DOI":"10.1109\/ICCV.2013.86"},{"key":"ref_16","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, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539821"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1109\/TCSVT.2015.2406194","article-title":"Robust Visual Tracking Using Structurally Random Projection and Weighted Least Squares","volume":"25","author":"Zhang","year":"2015","journal-title":"IEEE Trans Circuits Syst. Video Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.cviu.2016.05.011","article-title":"An occlusion-aware particle filter tracker to handle complex and persistent occlusions","volume":"150","author":"Meshgi","year":"2016","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, L., Yan, B., Lei, L., Cai, A., and Hu, G. (2016). Constrained Total Generalized p-Variation Minimization for Few-View X-Ray Computed Tomography Image Reconstruction. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0149899"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4842","DOI":"10.1109\/TIP.2016.2599290","article-title":"Weighted Schatten, p-Norm Minimization for Image Denoising and Background Subtraction","volume":"25","author":"Xie","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhang, H., Zhu, P., Zhang, D., and Zuo, W. (2015, January 14\u201316). Non-convex Regularized Self-representation for Unsupervised Feature Selection. Proceedings of the International Conference on Intelligent Science and Big Data Engineering, Suzhou, China.","DOI":"10.1007\/978-3-319-23862-3_6"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5810","DOI":"10.1109\/TSP.2012.2208955","article-title":"Nonconvex Splitting for Regularized Low-Rank + Sparse Decomposition","volume":"60","author":"Chartrand","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dash, P.P., Patra, D., and Mishra, S.K. (2014). Local Binary Pattern as a Texture Feature Descriptor in Object Tracking Algorithm. Intelligent Computing, Networking, and Informatics, Springer.","DOI":"10.1007\/978-81-322-1665-0_52"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yoon, J.H., Kim, D.Y., and Yoon, K.J. (2012, January 7\u201313). Visual tracking via adaptive tracker selection with multiple features. Proceedings of the 12th European Conference on Computer Vision, Florence, Italy.","DOI":"10.1007\/978-3-642-33765-9_3"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1109\/TPAMI.2007.70727","article-title":"Dependent Multiple Cue Integration for Robust Tracking","volume":"30","author":"Morenonoguer","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6313","DOI":"10.1016\/j.eswa.2010.11.111","article-title":"Multi-cue-based CamShift guided particle filter tracking","volume":"38","author":"Yin","year":"2011","journal-title":"Exp. Syst. Appl."},{"key":"ref_27","first-page":"1","article-title":"A fast particle filter object tracking algorithm by dual features fusion","volume":"9301","author":"Zhao","year":"2014","journal-title":"Proc. SPIE"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tian, P. (2015, January 23\u201325). A particle filter object tracking based on feature and location fusion. Proceedings of the IEEE International Conference on Software Engineering and Service Science, Beijing, China.","DOI":"10.1109\/ICSESS.2015.7339168"},{"key":"ref_29","unstructured":"Tseng, P. (2008). On accelerated proximal gradient methods for convex-concave optimization. SIAM J. Opt."},{"key":"ref_30","unstructured":"Zhong, W., Lu, H., and Yang, M.H. (2012, January 16\u201321). Robust object tracking via sparsity-based collaborative model. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_31","first-page":"127","article-title":"Fast Visual Tracking via Dense Spatio-temporal Context Learning","volume":"Volume 8693","author":"Zhang","year":"2014","journal-title":"Proceedings of the 13th European Conference on Computer Vision"},{"key":"ref_32","unstructured":"Caseiro, R., Martins, P., and Batista, J. (2012, January 7\u201313). Exploiting the Circulant Structure of Tracking-by-Detection with Kernels. Proceedings of the 12th European Conference on Computer Vision, Florence, Italy."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s11263-012-0582-z","article-title":"Robust visual tracking via structured multi-task sparse learning","volume":"101","author":"Zhang","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zhang, L., and Yang, M.H. (2012, January 7\u201313). Real-time compressive tracking. Proceedings of the 12th European Conference on Computer Vision, Florence, Italy.","DOI":"10.1007\/978-3-642-33712-3_62"},{"key":"ref_35","unstructured":"Laura, S.L., and Erik, L.M. (2012, January 16\u201321). Distribution fields for tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_36","unstructured":"Avidan, S., Levi, D., BarHillel, A., and Oron, S. (2012, January 16\u201321). Locally Orderless Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_37","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, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.312"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/9\/6\/140\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:07:55Z","timestamp":1760195275000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/9\/6\/140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,8]]},"references-count":37,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["info9060140"],"URL":"https:\/\/doi.org\/10.3390\/info9060140","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2018,6,8]]}}}