{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:23:20Z","timestamp":1760235800482,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T00:00:00Z","timestamp":1632441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["Grant No. FRF-GF-20-24B, FRF-MP-19-014"],"award-info":[{"award-number":["Grant No. FRF-GF-20-24B, FRF-MP-19-014"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory","award":["No. 311021013"],"award-info":[{"award-number":["No. 311021013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Visual tracking task is divided into classification and regression tasks, and manifold features are introduced to improve the performance of the tracker. Although the previous anchor-based tracker has achieved superior tracking performance, the anchor-based tracker not only needs to set parameters manually but also ignores the influence of the geometric characteristics of the object on the tracker performance. In this paper, we propose a novel Siamese network framework with ResNet50 as the backbone, which is an anchor-free tracker based on manifold features. The network design is simple and easy to understand, which not only considers the influence of geometric features on the target tracking performance but also reduces the calculation of parameters and improves the target tracking performance. In the experiment, we compared our tracker with the most advanced public benchmarks and obtained a state-of-the-art performance.<\/jats:p>","DOI":"10.3390\/s21196388","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T22:16:38Z","timestamp":1632780998000},"page":"6388","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SiamMFC: Visual Object Tracking Based on Mainfold Full Convolution Siamese Network"],"prefix":"10.3390","volume":"21","author":[{"given":"Jia","family":"Chen","sequence":"first","affiliation":[{"name":"National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Wang","sequence":"additional","affiliation":[{"name":"National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibo","family":"Ai","sequence":"additional","affiliation":[{"name":"National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, H., Xiezhang, T., Yang, C., Deng, L., and Yi, P. (2021). Secure video surveillance framework in smart city. Sensors, 21.","DOI":"10.3390\/s21134419"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, R., Ouyang, Q., Cui, Y., and Jin, Y. (2021). Preview control with dynamic constraints for autonomous vehicles. Sensors, 21.","DOI":"10.3390\/s21155155"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1049\/iet-its.2019.0536","article-title":"Multiple object tracking using a dual-attention network for autonomous driving","volume":"14","author":"Gao","year":"2020","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2764","DOI":"10.1177\/09544070211006520","article-title":"A novel Siamese Attention Network for visual object tracking of autonomous vehicles","volume":"235","author":"Chen","year":"2021","journal-title":"Proc. Inst. Mech. Eng. D J. Automob. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tao, R., Gavves, E., and Smeulders, A.W.M. (2016, January 27\u201330). Siamese Instance Search for Tracking. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.158"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun ACM"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, B., Yan, J., Wu, W., Zhu, Z., and Hu, X. (2018, January 18\u201323). High Performance Visual Tracking with Siamese Region Proposal Network. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00935"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., and Yan, J. (2019, January 15\u201320). SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00441"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., Ren, S.Q., and Sun, J. (2016, January 17\u201322). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, New York, NY, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.cviu.2013.09.007","article-title":"Object tracking using learned feature manifolds","volume":"118","author":"Guo","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1137\/S0895479895290954","article-title":"The geometry of algorithms with orthogonality constraints","volume":"20","author":"Edelman","year":"1999","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"ref_12","unstructured":"Declercq, A., and Piater, J.H. (2008, January 22\u201325). Online learning of gaussian mixture models\u2014A two-level approach. Proceedings of the 3rd International Conference on Computer Vision Theory Applications, Funchal, Portugal."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yu, J., Jiang, Y., Wang, Z., Cao, Z., and Huang, T. (2016;, January 15\u201319). UnitBox: An Advanced Object Detection Network. Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, The Netherlands.","DOI":"10.1145\/2964284.2967274"},{"key":"ref_14","unstructured":"Tian, Z., Shen, C.H., Chen, H., and He, T. (November, January 27). FCOS: Fully Convolutional One-Stage Object Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Microsoft COCO: Common Objects in Context. Proceedings of the 13th European Conference on Computer Vision (ECCV), Zurich, Switzerland, 6\u201312 September 2014, Springer International Publishing AG.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Real, E., Shlens, J., Mazzocchi, S., Pan, X., and Vanhoucke, V. YouTube-Bounding-Boxes: A large high-precision human annotated data set for object detection in video. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.789"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Shahbaz Khan, F., and Felsberg, M. (2017, January 21\u201326). ECO: Efficient Convolution Operators for Tracking. Proceedings of the 30th IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.733"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hong, Z., Chen, Z., Wang, C., Mei, X., Prokhorov, D., and Tao, D. (2015, January 7\u201312). MUlti-Store Tracker (MUSTer): A Cognitive Psychology Inspired Approach to Object Tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298675"},{"key":"ref_20","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.156"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1109\/TPAMI.2015.2509974","article-title":"Struck: Structured Output Tracking with Kernels","volume":"38","author":"Hare","year":"2016","journal-title":"IEEE Trans. Pattern. Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Khan, F.S., and Felsberg, M. (2015, January 7\u201313). Convolutional features for correlation filter based visual tracking. Proceedings of the IEEE International Conference on Computer Vision Workshop (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.84"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"89777","DOI":"10.1109\/ACCESS.2019.2927211","article-title":"Distractor-aware visual tracking by online Siamese network","volume":"7","author":"Zha","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","unstructured":"Huang, L., Zhao, X., and Huang, K. (2018). Got-10k: A large high-diversity benchmark for generic object tracking in the wild. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Robinson, A., Khan, F.S., and Felsberg, M. (2016). Beyond correlation filters: Learning continuous convolution operators for visual tracking. Proceedings of the 14th European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, 8\u201316 October 2016, Springer International Publishing AG.","DOI":"10.1007\/978-3-319-46454-1_29"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nam, H., and Han, B. (2016, January 27\u201330). Learning multi-domain convolutional neural networks for visual tracking. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.465"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., and Torr, P.H.S. (2017, January 21\u201326). End-to-end representation learning for correlation filter based tracking. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.531"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, G., Luo, C., Xiong, Z., and Zeng, W. (2019, January 16\u201321). SPM-tracker: Series-parallel matching for real-time visual object tracking. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00376"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Galoogahi, H.K., Fagg, A., and Lucey, S. (2017, January 22\u201329). Learning background-aware correlation filters for visual tracking. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.129"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, J.M., Ma, S.G., and Sclaroff, S. (2014). MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization. Proceedings of the 13th European Conference on Computer Vision (ECCV), Zurich, Switzerland, 6\u201312 September 2014, Springer.","DOI":"10.1007\/978-3-319-10599-4_13"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6388\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:04:29Z","timestamp":1760166269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6388"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,24]]},"references-count":30,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["s21196388"],"URL":"https:\/\/doi.org\/10.3390\/s21196388","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,9,24]]}}}