{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:28:14Z","timestamp":1760149694281,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:00:00Z","timestamp":1693353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Visual object tracking is a fundamental task in computer vision that requires estimating the position and scale of a target object in a video sequence. However, scale variation is a difficult challenge that affects the performance and robustness of many trackers, especially those based on the discriminative correlation filter (DCF). Existing scale estimation methods based on multi-scale features are computationally expensive and degrade the real-time performance of the DCF-based tracker, especially in scenarios with restricted computing power. In this paper, we propose a practical and efficient solution that can handle scale changes without using multi-scale features and can be combined with any DCF-based tracker as a plug-in module. We use color name (CN) features and a salient feature to reduce the target appearance model\u2019s dimensionality. We then estimate the target scale based on a Gaussian distribution model and introduce global and local scale consistency assumptions to restore the target\u2019s scale. We fuse the tracking results with the DCF-based tracker to obtain the new position and scale of the target. We evaluate our method on the benchmark dataset Temple Color 128 and compare it with some popular trackers. Our method achieves competitive accuracy and robustness while significantly reducing the computational cost.<\/jats:p>","DOI":"10.3390\/s23177516","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T10:30:52Z","timestamp":1693391452000},"page":"7516","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Scale-Aware Tracking Method with Appearance Feature Filtering and Inter-Frame Continuity"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6044-897X","authenticated-orcid":false,"given":"Haiyu","family":"He","sequence":"first","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing 100010, China"}]},{"given":"Zhen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing 100010, China"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing 100010, China"}]},{"given":"Xiangdong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing 100010, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0458-722X","authenticated-orcid":false,"given":"Haikuo","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100010, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5191","DOI":"10.1109\/LRA.2021.3068640","article-title":"DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM","volume":"6","author":"Bescos","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4281","DOI":"10.1109\/LRA.2021.3067285","article-title":"Visual Servoing of a Cable-Driven Soft Robot Manipulator With Shape Feature","volume":"6","author":"Xu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"23963","DOI":"10.1007\/s11042-021-10804-4","article-title":"A visual tracking algorithm via confidence-based multi-feature correlation filtering","volume":"80","author":"Fang","year":"2021","journal-title":"Multimed. Tools. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3248","DOI":"10.1109\/LRA.2021.3062010","article-title":"Learning Occupancy Priors of Human Motion from Semantic Maps of Urban Environments","volume":"6","author":"Rudenko","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102765","DOI":"10.1016\/j.scs.2021.102765","article-title":"A novel social distancing analysis in urban public space: A new online spatio-temporal trajectory approach","volume":"68","author":"Su","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11577","DOI":"10.1109\/JSEN.2020.3037301","article-title":"Supervised Scene Illumination Control in Stereo Arthroscopes for Robot Assisted Minimally Invasive Surgery","volume":"21","author":"Ali","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3979","DOI":"10.1109\/LRA.2021.3066834","article-title":"Deep Learning Assisted Robotic Magnetic Anchored and Guided Endoscope for Real-Time Instrument Tracking","volume":"6","author":"Cheng","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107822","DOI":"10.1016\/j.patcog.2021.107822","article-title":"Visual SLAM for robot navigation in healthcare facility","volume":"113","author":"Fang","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_9","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_10","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_11","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1007\/978-3-319-16181-5_18","article-title":"A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration","volume":"Volume 8926","author":"Li","year":"2015","journal-title":"Computer Vision-ECCV 2014 Workshops: Zurich, Switzerland, September 6\u20137 and 12, 2014, Proceedings, Part II 13"},{"key":"ref_12","first-page":"6552","article-title":"Visual Object Tracking With Discriminative Filters and Siamese Networks: A Survey and Outlook","volume":"45","author":"Javed","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"7128","DOI":"10.1109\/TIP.2020.2998978","article-title":"Fast Learning of Spatially Regularized and Content Aware Correlation Filter for Visual Tracking","volume":"29","author":"Han","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/LRA.2021.3119379","article-title":"ROFT: Real-Time Optical Flow-Aided 6D Object Pose and Velocity Tracking","volume":"7","author":"Piga","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Khan, F., 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), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.143"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1109\/TPAMI.2018.2829180","article-title":"Learning Support Correlation Filters for Visual Tracking","volume":"41","author":"Zuo","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1109\/TPAMI.2018.2797062","article-title":"Learning Multi-Task Correlation Particle Filters for Visual Tracking","volume":"41","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Bolme, D., Beveridge, J., Draper, B., and Lui, Y. (2010, January 13\u201318). Visual Object Tracking using Adaptive Correlation Filters. Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/LSP.2019.2963147","article-title":"FAST: Fast and Accurate Scale Estimation for Tracking","volume":"27","author":"Ma","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1109\/TCSVT.2019.2944654","article-title":"Adaptive Region Proposal With Channel Regularization for Robust Object Tracking","volume":"31","author":"Lu","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.patrec.2014.03.025","article-title":"Robust scale-adaptive mean-shift for tracking","volume":"49","author":"Vojir","year":"2014","journal-title":"Pattern Recogn. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1109\/TIP.2022.3148876","article-title":"Siamese Implicit Region Proposal Network With Compound Attention for Visual Tracking","volume":"31","author":"Chan","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/TCSVT.2021.3109981","article-title":"Dynamic Particle Filter Framework for Robust Object Tracking","volume":"32","author":"Li","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1109\/TIP.2022.3232941","article-title":"Part-Aware Framework for Robust Object Tracking","volume":"32","author":"Li","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.neucom.2022.09.028","article-title":"Unveil the potential of siamese framework for visual tracking","volume":"513","author":"Yang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_28","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":"Schmid","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5630","DOI":"10.1109\/TIP.2015.2482905","article-title":"Encoding Color Information for Visual Tracking: Algorithms and Benchmark","volume":"24","author":"Liang","year":"2015","journal-title":"IEEE Trans. Image Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7516\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:42:35Z","timestamp":1760128955000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7516"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,30]]},"references-count":29,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23177516"],"URL":"https:\/\/doi.org\/10.3390\/s23177516","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,8,30]]}}}