{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T04:44:19Z","timestamp":1777524259600,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,5,13]],"date-time":"2019-05-13T00:00:00Z","timestamp":1557705600000},"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>SiamFC has a simple network structure and can be pretrained offline on a large data set, so it has attracted the attention of many researchers. It has no online learning process at all. Hence, there are no good solutions for some complex tracking scenarios such as occlusion and large target deformation. For this problem, we propose a method using the Kalman filter method and fusion multiresolution features and get multiple response scores. The Kalman filter acquires the target\u2019s trajectory information, which is used to process complex tracking scenes and to change the selection method of the search area. This also enables our tracker to stably track fast moving targets.The introduction of the Kalman filter compensates for the shortcomings that SiamFC can only track offline, and the tracking network has an online learning process. The fusion of multiresolution features to obtain multiple response scores map helps the tracker to obtain robust features that can be adapted to a variety of tracking targets. Our proposed method has reached the state-of-the-art in testing on five data sets and can be run in real time (40 fps), including OTB2013, OTB2015, OTB50, VOT2015 and VOT 2016.<\/jats:p>","DOI":"10.3390\/s19092201","type":"journal-article","created":{"date-parts":[[2019,5,13]],"date-time":"2019-05-13T05:35:39Z","timestamp":1557725739000},"page":"2201","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Combined Kalman Filter and Multifeature Fusion Siamese Network for Real-Time Visual Tracking"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5795-855X","authenticated-orcid":false,"given":"Lijun","family":"Zhou","sequence":"first","affiliation":[{"name":"Key Laboratory of Optical Engineering, Institute of Optics and Electronics, Chinese Academy of Sciences, No.1, Optoelectronic Avenue, Wenxing Town, Shuangliu District, Chengdu 610200, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5284-2942","authenticated-orcid":false,"given":"Jianlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Optical Engineering, Institute of Optics and Electronics, Chinese Academy of Sciences, No.1, Optoelectronic Avenue, Wenxing Town, Shuangliu District, Chengdu 610200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,13]]},"reference":[{"key":"ref_1","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 Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.312"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","article-title":"Object tracking benchmark","volume":"37","author":"Yi","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","unstructured":"Kristan, M., Matas, J., Leonardis, A., Felsberg, M., Cehovin, L., Fernez, G., Vojir, T., Hager, G., and Nebehay, G. (2015, January 7\u201313). The Visual Object Tracking VOT2015 challenge results. Proceedings of the IEEE International Conference on Computer Vision Workshop, Santiago, Chile."},{"key":"ref_4","unstructured":"Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Cehovin, L., Vojir, T., Hager, G., Lukezic, A., and Eldesokey, A. (2016, January 8\u201310). The Visual Object Tracking VOT2016 challenge results. Proceedings of the European Conference on Computer VisionWorkshops, Amsterdam, The Netherlands."},{"key":"ref_5","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.","DOI":"10.1109\/CVPR.2017.733"},{"key":"ref_6","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, Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.84"},{"key":"ref_7","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, Springer International Publishing.","DOI":"10.1007\/978-3-319-46454-1_29"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ma, C., Huang, J.B., Yang, X., and Yang, M.H. (2015, January 7\u201313). Hierarchical convolutional features for visual tracking. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.352"},{"key":"ref_9","unstructured":"Qi, Y., Zhang, S., Qin, L., Yao, H., Huang, Q., Lim, J., and Yang, M. (July, January 26). Hedged deep tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_10","unstructured":"Nam, H., Baek, M., and Han, B. (2016). Modeling and propagating cnns in a tree structure for visual tracking. arXiv."},{"key":"ref_11","unstructured":"Nam, H., and Han, B. (July, January 26). 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_12","doi-asserted-by":"crossref","unstructured":"Han, B., Sim, J., and Adam, H. (2017, January 21\u201326). Branchout: Regularization for online ensemble tracking with convolutional neural networks. Proceedings of the Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.63"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gladh, S., Danelljan, M., Khan, F.S., and Felsberg, M. (2016, January 4\u20138). Deep motion features for visual tracking. Proceedings of the International Conference on Pattern Recognition, Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7899807"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Doulamis, N., and Doulamis, A. (2012). Fast and Adaptive Deep Fusion Learning for Detecting Visual Objects. Computer Vision\u2014ECCV 2012. Workshops and Demonstrations, Proceedings of the European Conference on Computer Vision, Florence, Italy, 7\u201313 October 2012, Springer.","DOI":"10.1007\/978-3-642-33885-4_35"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Doulamis, N., and Voulodimos, A. (2016, January 4\u20136). FAST-MDL: Fast Adaptive Supervised Training of multi-layered deep learning models for consistent object tracking and classification. Proceedings of the Conference on Imaging Systems and Techniques (IST), Chania, Greece.","DOI":"10.1109\/IST.2016.7738244"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., and Torr, P.H. (2016). Fully-convolutional siamese networks for object tracking. Computer Vision\u2014ECCV 2016 Workshops, Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands, 8\u201316 October 2016, Springer.","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., and Wang, S. (2017, January 22\u201329). Learning dynamic siamese network for visual object tracking. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.196"},{"key":"ref_18","unstructured":"Chen, H., Lucey, S., and Ramanan, D. (2017, January 22\u201329). Learning policies for adaptive tracking with deep feature cascades. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy."},{"key":"ref_19","unstructured":"Tao, R., Gavves, E., and Smeulders, A.W.M. (July, January 26). Siamese instance search for tracking. Proceedings of the Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xu, H., Gao, Y., Yu, F., and Darrell, T. (2017, January 21\u201326). End-to-end learning of driving models from large-scale video datasets. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.376"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, T., and Chan, A.B. (2017, January 22\u201329). Recurrent filter learning for visual tracking. Proceedings of the IEEE International Conference on Computer Vision Workshop, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.235"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, A., Luo, C., Tian, X., and Zeng, W. (2018, January 18\u201322). A twofold siamese network for real-time object tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00508"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7068349","DOI":"10.1155\/2018\/7068349","article-title":"Deep learning for computer vision: A brief review","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep learning for visual understanding: A review","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.patrec.2014.01.008","article-title":"A review of unsupervised feature learning and deep learning for time-series modeling","volume":"42","author":"Karlsson","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_27","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutionalneural networks. Proceedings of the International Conference on NeuralInformation Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_28","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_29","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 9\u201312). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the International Conference on Neural Information Processing Systems, Istanbul, Turkey."},{"key":"ref_30","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 International Conference on International Conference on Machine Learning, Lille, France."},{"key":"ref_31","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":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., and Lu, H. (2015, January 7\u201313). Visual tracking with fully convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.357"},{"key":"ref_33","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, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.531"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Held, D., Thrun, S., and Savarese, S. (2016, January 11\u201314). Learning to track at 100 fps with deep regression networks. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_45"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Song, Y., Ma, C., Gong, L., Zhang, J., Lau, R.W.H., and Yang, M. (2017, January 22\u201329). Crest: Convolutional residual learning for visual tracking. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.279"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Wu, W., Zou, W., and Yan, J. (2018, January 18\u201322). End-toend flow correlation tracking with spatial-temporal attention. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00064"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, Q., Teng, Z., Xing, J., Gao, J., Hu, W., and Maybank, S. (2018, January 18\u201322). Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking. Proceedings of the Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00510"},{"key":"ref_38","first-page":"3","article-title":"Speed and Hardware Optimization of ORDP Algorithm Based Kalman Filter for 2D Object Tracking","volume":"4","author":"Kumar","year":"2014","journal-title":"Int. J. Curr. Eng. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1016\/j.jvcir.2006.03.004","article-title":"Video object tracking using adaptive Kalman filter","volume":"17","author":"Weng","year":"2006","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"020503","DOI":"10.1117\/1.3327281","article-title":"Object tracking using an adaptive Kalman filter combined with mean shift","volume":"49","author":"Li","year":"2010","journal-title":"Opt. Eng."},{"key":"ref_41","first-page":"1","article-title":"Optimized Neural Network Parameters Using Stochastic Fractal Technique to Compensate Kalman Filter for Power System-Tracking-State Estimation","volume":"99","author":"Hossam","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_42","first-page":"012068","article-title":"Real-time vehicle detection and tracking in video based on faster R-CNN","volume":"887","author":"Zhang","year":"2017","journal-title":"J. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mozhdehi, R.J., and Medeiros, H. (2017, January 17\u201320). Deep convolutional particle filter for visual tracking. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296963"},{"key":"ref_44","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":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_45","unstructured":"Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., and Torr, P.H.S. (July, January 26). Staple: Complementary learners for real-time tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ma, S., and Sclaroff, S. (2014). Meem: Robust tracking via multiple experts using entropy minimization. Computer Vision\u2014ECCV2014, Proceedings of the European Conference on Computer Vision, Zurich, Switzerland, 6\u201312 September 2014, Springer International Publishing.","DOI":"10.1007\/978-3-319-10599-4_13"},{"key":"ref_47","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_48","unstructured":"Hauml, M.D.G., Khan, g.F.S., and Felsberg, M. (2014, January 1\u20135). Accurate scale estimation for robust visual tracking. Proceedings of the British Machine Vision Conference, Nottingham, UK."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/9\/2201\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:51:24Z","timestamp":1760187084000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/9\/2201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,13]]},"references-count":48,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["s19092201"],"URL":"https:\/\/doi.org\/10.3390\/s19092201","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,13]]}}}