{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:01:20Z","timestamp":1760241680407,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,20]],"date-time":"2018-07-20T00:00:00Z","timestamp":1532044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. The existing spatially regularized discriminative correlation filter (SRDCF) method learns partial-target information or background information when experiencing rotation, out of view, and heavy occlusion. In order to reduce the computational complexity by creating a novel method to enhance tracking ability, we first introduce an adaptive dimensionality reduction technique to extract the features from the image, based on pre-trained VGG-Net. We then propose an adaptive model update to assign weights during an update procedure depending on the peak-to-sidelobe ratio. Finally, we combine the online SRDCF-based tracker with the offline Siamese tracker to accomplish long term tracking. Experimental results demonstrate that the proposed tracker has satisfactory performance in a wide range of challenging tracking scenarios.<\/jats:p>","DOI":"10.3390\/s18072359","type":"journal-article","created":{"date-parts":[[2018,7,23]],"date-time":"2018-07-23T03:24:27Z","timestamp":1532316267000},"page":"2359","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Robust Visual Tracking Based on Adaptive Convolutional Features and Offline Siamese Tracker"],"prefix":"10.3390","volume":"18","author":[{"given":"Ximing","family":"Zhang","sequence":"first","affiliation":[{"name":"Academy of Astronautics, Northwestern Polytechnical University, YouYi Street, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingang","family":"Wang","sequence":"additional","affiliation":[{"name":"Academy of Astronautics, Northwestern Polytechnical University, YouYi Street, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,20]]},"reference":[{"key":"ref_1","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."},{"doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Khan, F.S., and Michael, F. (2015, January 13\u201315). Learning Spatially Regularized Correlation Filters for Visual Tracking. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","key":"ref_2","DOI":"10.1109\/ICCV.2015.490"},{"doi-asserted-by":"crossref","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F., and Felsberg, M. (2014, January 1\u20135). Accurate Scale Estimation for Robust Visual Tracking. Proceedings of the British Machine Vision Conference (BMVC), Nottingham, UK.","key":"ref_3","DOI":"10.5244\/C.28.65"},{"key":"ref_4","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":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Ma, C., Yang, X., Zhang, C., and Yang, M.H. (2015, January 7\u201312). Long-term correlation tracking. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","key":"ref_5","DOI":"10.1109\/CVPR.2015.7299177"},{"doi-asserted-by":"crossref","unstructured":"Galoogahi, H.K., Sim, T., and Lucey, S. (2015, January 7\u201312). Correlation filters with limited boundaries. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","key":"ref_6","DOI":"10.1109\/CVPR.2015.7299094"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1702","DOI":"10.1109\/TPAMI.2014.2375215","article-title":"Zero-Aliasing Correlation Filters for Object Recognition","volume":"37","author":"Fernandez","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"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.","key":"ref_8","DOI":"10.1109\/ICCV.2011.6126251"},{"unstructured":"Wang, N., and Yeung, D.Y. (2013, January 3\u20137). Learning a deep compact image representation for visual tracking. Proceedings of the International Conference on Neural Information Processing Systems, Daegu, Korea.","key":"ref_9"},{"unstructured":"Wang, S., Lu, H., Yang, F., and Yang, M.H. (2011, January 6\u201313). Superpixel tracking. Proceedings of the International Conference on Computer Vision (ICCV), Barcelona, Spain.","key":"ref_10"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1109\/TIP.2013.2293430","article-title":"Robust Online Learned Spatio-Temporal Context Model for Visual Tracking","volume":"23","author":"Wen","year":"2014","journal-title":"IEEE Trans. Image Process."},{"unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. Comput. Sci.","key":"ref_12"},{"unstructured":"Adam, A., Rivlin, E., and Shimshoni, I. (2006, January 17\u201322). Robust Fragments-based Tracking using the Integral Histogram. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), New York, NY, USA.","key":"ref_13"},{"doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Khan, F.S., and Felsberg, M. (2015, January 13\u201315). Convolutional Features for Correlation Filter Based Visual Tracking. Proceedings of the IEEE International Conference on Computer Vision Workshop (ICCV), Santiago, Chile.","key":"ref_14","DOI":"10.1109\/ICCVW.2015.84"},{"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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","key":"ref_15","DOI":"10.1109\/CVPR.2017.531"},{"unstructured":"Wang, N., Li, S., Gupta, A., and Yeung, D.-Y. (2015). Transferring Rich Feature Hierarchies for Robust Visual Tracking. Comput. Sci.","key":"ref_16"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1109\/TED.2016.2529656","article-title":"Robust Visual Tracking via Convolutional Networks without Training","volume":"25","author":"Zhang","year":"2016","journal-title":"IEEE Trans Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1109\/TIP.2015.2510583","article-title":"DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking","volume":"25","author":"Li","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","first-page":"1834","article-title":"Hierarchical Convolutional Features for Visual Tracking","volume":"25","author":"Ma","year":"2015","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Danelljan, M., Robinson, A., Khan, F.S., and Felsberg, M. (2016, January 11\u201314). Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","key":"ref_20","DOI":"10.1007\/978-3-319-46454-1_29"},{"doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Khan, F.S., and Felsberg, M. (2017, January 21\u201326). ECO: Efficient Convolution Operators for Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","key":"ref_21","DOI":"10.1109\/CVPR.2017.733"},{"doi-asserted-by":"crossref","unstructured":"Danelljan, M., Khan, F.S., Felsberg, M., and van de Weijer, J. (2014, January 24\u201327). Adaptive Color Attributes for Real-Time Visual Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","key":"ref_22","DOI":"10.1109\/CVPR.2014.143"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"33","DOI":"10.4236\/jcc.2015.311006","article-title":"Semi-Supervised Dimensionality Reduction of Hyperspectral Image Based on Sparse Multi-Manifold Learning","volume":"3","author":"Huang","year":"2017","journal-title":"J. Comput. Commun."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"0315002","DOI":"10.3788\/AOS201737.0315002","article-title":"Visual Tracking Algorithm Based on Adaptive Convolutional Features","volume":"37","author":"Cai","year":"2017","journal-title":"Acta Opt. Sin."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/TPAMI.2017.2655048","article-title":"Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods","volume":"40","author":"Harandi","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Tao, R., Gavves, E., and Smeulders, A.W.M. (2016, January 26\u201331). Siamese instance search for tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","key":"ref_26","DOI":"10.1109\/CVPR.2016.158"},{"doi-asserted-by":"crossref","unstructured":"Held, D., Thrun, S., and Savarese, S. (2016, January 26\u201331). Learning to track at 100 fps with deep regression networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","key":"ref_27","DOI":"10.1007\/978-3-319-46448-0_45"},{"unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., and Torr, P.H.S. (2016, January 26\u201331). Fully-Convolutional Siamese Networks for Object Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","key":"ref_28"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2014","journal-title":"Int. J. Comput. Vision"},{"unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 12\u201315). ImageNet classification with deep convolutional neural networks. Proceedings of the International Conference on Neural Information Processing Systems, Doha, Qatar.","key":"ref_30"},{"doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., and Anguelov, D. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","key":"ref_31","DOI":"10.1109\/CVPR.2015.7298594"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 26\u201331). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","key":"ref_32","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","first-page":"2544","article-title":"Visual object tracking using adaptive correlation filters","volume":"119","author":"Bolme","year":"2010","journal-title":"IEEE Comput. Vision Pattern Recogn."},{"key":"ref_34","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."},{"doi-asserted-by":"crossref","unstructured":"Vedaldi, A., and Lenc, K. (2015, January 26\u201330). MatConvNet: Convolutional Neural Networks for MATLAB. Proceedings of the ACM International Conference on Multimedia, Brisbane, Australia.","key":"ref_35","DOI":"10.1145\/2733373.2807412"},{"key":"ref_36","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_37","first-page":"188","article-title":"Transfer Learning Based Visual Tracking with Gaussian Processes Regression","volume":"8691","author":"Gao","year":"2014","journal-title":"Springer"},{"doi-asserted-by":"crossref","unstructured":"Li, Y., and Zhu, J.A. (2014, January 6\u201312). Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration. Proceedings of the IEEE European Conference on Computer Vision Workshops (ECCV), Zurich, Switzerland.","key":"ref_38","DOI":"10.1007\/978-3-319-16181-5_18"},{"doi-asserted-by":"crossref","unstructured":"Li, Y., Zhu, J., and Hoi, S.C. (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.","key":"ref_39","DOI":"10.1109\/CVPR.2015.7298632"},{"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. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","key":"ref_40","DOI":"10.1109\/CVPR.2015.7298675"},{"key":"ref_41","first-page":"1261","article-title":"Visual Object Tracking Performance Measures Revisited","volume":"25","author":"Leonardis","year":"2016","journal-title":"IEEE Signal Process. Soc."},{"doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., and Torr, P.H.S. (2016, January 26\u201331). Staple: Complementary Learners for Real-Time Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","key":"ref_42","DOI":"10.1109\/CVPR.2016.156"},{"unstructured":"Roffo, G., Kristan, M., and Matas, J. (2016, January 8\u201316). The Visual Object Tracking VOT2016 challenge results. Proceedings of the IEEE European Conference on Computer Vision Workshops (ECCV), Amsterdam, The Netherlands.","key":"ref_43"},{"doi-asserted-by":"crossref","unstructured":"Nam, H., and Han, B. (2016, January 26\u201331). Learning multi-domain convolutional neural networks for visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","key":"ref_44","DOI":"10.1109\/CVPR.2016.465"},{"doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., and Lu, H. (2015, January 13\u201315). Visual tracking with fully convolutional networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","key":"ref_45","DOI":"10.1109\/ICCV.2015.357"},{"doi-asserted-by":"crossref","unstructured":"Zhu, G., Porikli, F., and Li, H. (2016, January 26\u201331). Beyond local search: Tracking objects everywhere with instance-specific proposals. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","key":"ref_46","DOI":"10.1109\/CVPR.2016.108"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/7\/2359\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:13:19Z","timestamp":1760195599000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/7\/2359"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,20]]},"references-count":46,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2018,7]]}},"alternative-id":["s18072359"],"URL":"https:\/\/doi.org\/10.3390\/s18072359","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,7,20]]}}}