{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:03:31Z","timestamp":1760241811106,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,8,21]],"date-time":"2018-08-21T00:00:00Z","timestamp":1534809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2016YFB0502502"],"award-info":[{"award-number":["2016YFB0502502"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation Project for Advanced Research Field","award":["614023804016HK03002"],"award-info":[{"award-number":["614023804016HK03002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the increasing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), visual tracking using UAVs has become more and more important due to its many new applications, including automatic navigation, obstacle avoidance, traffic monitoring, search and rescue, etc. However, real-world aerial tracking poses many challenges due to platform motion and image instability, such as aspect ratio change, viewpoint change, fast motion, scale variation and so on. In this paper, an efficient object tracking method for UAV videos is proposed to tackle these challenges. We construct the fused features to capture the gradient information and color characteristics simultaneously. Furthermore, cellular automata is introduced to update the appearance template of target accurately and sparsely. In particular, a high confidence model updating strategy is developed according to the stability function. Systematic comparative evaluations performed on the popular UAV123 dataset show the efficiency of the proposed approach.<\/jats:p>","DOI":"10.3390\/s18092751","type":"journal-article","created":{"date-parts":[[2018,8,21]],"date-time":"2018-08-21T11:12:42Z","timestamp":1534849962000},"page":"2751","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model"],"prefix":"10.3390","volume":"18","author":[{"given":"Xizhe","family":"Xue","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, Shaanxi, China"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, Shaanxi, China"}]},{"given":"Qiang","family":"Shen","sequence":"additional","affiliation":[]}],"member":"1968","published-online":{"date-parts":[[2018,8,21]]},"reference":[{"key":"ref_1","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."},{"key":"ref_2","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.","DOI":"10.1109\/ICCV.2011.6126251"},{"key":"ref_3","unstructured":"Lu, H., Jia, X., and Yang, M.H. (2012, January 16\u201321). Visual tracking via adaptive structural local sparse appearance model. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1109\/TPAMI.2015.2465908","article-title":"What Makes for Effective Detection Proposals?","volume":"38","author":"Hosang","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Weinberger, K.Q., and van der Marten, L. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_6","unstructured":"Borji, A., Cheng, M., and Hou, Q. (arXiv, 2014). Salient Object Detection: A Survey, arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Islam, M.M., Hu, G., and Liu, Q. (2018). Online Model Updating and Dynamic Learning Rate-Based Robust Object Tracking. Sensors, 18.","DOI":"10.3390\/s18072046"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, F., Zhang, S., and Qiao, X. (2017). Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters. Sensors, 17.","DOI":"10.3390\/s17112626"},{"key":"ref_9","unstructured":"Blake, A., and Isard, M. (2012). Active Contours: The Application of Techniques from Graphics, Vision, Control Theory and Statistics to Visual Tracking of Shapes in Motion, Springer Science Business Media."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7263","DOI":"10.1016\/j.eswa.2015.05.055","article-title":"An integrated system for vehicle tracking and classification","volume":"42","author":"Battiato","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Andriluka, M., Roth, S., and Schiele, B. (2008, January 23\u201328). People-tracking-by-detection and people-detection-by-tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587583"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fu, C., Duan, R., and Kircali, D. (2016). Onboard Robust Visual Tracking for UAVs Using a Reliable Global-Local Object Model. Sensors, 16.","DOI":"10.3390\/s16091406"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fu, C., Suarez-Fernandez, R., Olivares-Mendez, M., and Campoy, P. (2013, January 20\u201322). Real-time adaptive multi-classifier multi-resolution visual tracking framework for unmanned aerial vehicles. Proceedings of the 2nd Workshop on Research, Development and Education on Unmanned Aerial Systems (RED-UAS), Compiegne, France.","DOI":"10.3182\/20131120-3-FR-4045.00010"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lim, H., and Sinha, S.N. (2015, January 26\u201330). Monocular localization of a moving person onboard a Quadrotor MAV. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139487"},{"key":"ref_15","unstructured":"Fu, C., Carrio, A., Olivares-Mendez, M., Suarez-Fernandez, R., and Campoy, P. (June, January 31). Robust real-time vision-based aircraft tracking from Unmanned Aerial Vehicles. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China."},{"key":"ref_16","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 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299177"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhu, G., Wang, J., Wu, Y., and Lu, H. (2015, January 7\u201310). Collaborative Correlation Tracking. Proceedings of the British Machine Vision Conference, Swansea, UK.","DOI":"10.5244\/C.29.184"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., Draper, B.A., and Lui, Y.M. (2010, January 13\u201318). Visual object tracking using adaptive correlation filters. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Henriques, F., Caseiro, R., Martins, P., and Batista, J. (2012, January 7\u201313). Exploiting the Circulant Structure of Tracking-by-Detection with Kernels. Proceedings of the European Conference on Computer Vision (ECCV), Florence, Italy.","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Boddeti, V.N., Kanade, T., and Kumar, B.V. (2013, January 23\u201328). Correlation filters for object alignment. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.297"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1109\/TIP.2017.2775060","article-title":"Latent constrained correlation filter","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, L., Chen, C., Wang, H., Zhang, B., and Han, J. (2016). Adaptive Multi-class Correlation Filters. Advances in Multimedia Information Processing\u2014PCM, Springer International Publishing.","DOI":"10.1007\/978-3-319-48896-7_67"},{"key":"ref_23","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, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.465"},{"key":"ref_24","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 2016 European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_45"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Wu, W., Zou, W., and Yan, J. (arXiv, 2017). End-to-end flow correlation tracking with spatial-temporal attention. illumination, arXiv.","DOI":"10.1109\/CVPR.2018.00064"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., and Henriques, J.F. (2016, January 8\u201310). Fully-convolutional siamese networks for object tracking. Proceedings of the 2016 European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1109\/TIP.2016.2520358","article-title":"BIT: Biologically Inspired Tracker","volume":"25","author":"Cai","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Khan, F.S., Felsberg, M., and van de Weijer, J. (2014, January 23\u201328). Adaptive Color Attributes for Real-Time Visual Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.143"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Khan, F.S., and Felsberg, M. (2014, January 1\u20135). Accurate scale estimation for robust visual tracking. Proceedings of the British Machine Vision Conference (BMVC), Nottingham, UK.","DOI":"10.5244\/C.28.65"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., and Torr, P.H.S. (2016, January 27\u201330). Staple: Complementary Learners for Real-Time Tracking. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.156"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Khan, F.S., and Felsberg, M. (2016, January 7\u201313). Learning Spatially Regularized Correlation Filters for Visual Tracking. Proceedings of the 2016 IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.490"},{"key":"ref_33","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 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.733"},{"key":"ref_34","unstructured":"Von Neumann, J. (1951). The general and logical theory of automata. Cerebral Mechanisms in Behavior, Wiley."},{"key":"ref_35","unstructured":"Qin, Y., Lu, H., Xu, Y., and Wang, H. (2015, January 7\u201312). Saliency detection via Cellular Automata. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Khan, R., Weijer, J.V.D., Khan, F.S., Muselet, D., Ducottet, C., and Barat, C. (2013, January 23\u201328). Discriminative Color Descriptors. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.369"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, M., Liu, Y., and Huang, Z. (2017, January 21\u201326). Large Margin Object Tracking with Circulant Feature Maps. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.510"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mueller, M., Smith, N., and Ghanem, B. (2016, January 11\u201314). A Benchmark and Simulator for UAV Tracking. Proceedings of the 2016 European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_27"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","unstructured":"Huang, D., Luo, L., Wen, M., and Chen, Z. (2015, January 7\u201310). Enable Scale and Aspect Ratio Adaptability in Visual Tracking with Detection Proposals. Proceedings of the 2015 British Machine Vision Conference, Swansea, UK.","DOI":"10.5244\/C.29.185"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, Y., and Zhu, J. (2014, January 6\u20137). A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration. Proceedings of the 2014 European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-16181-5_18"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1109\/TSMC.2016.2629509","article-title":"Output constraint transfer for kernelized correlation filter in tracking","volume":"47","author":"Baochang","year":"2017","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_43","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_44","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 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.312"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/9\/2751\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:20:10Z","timestamp":1760196010000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/9\/2751"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,21]]},"references-count":44,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2018,9]]}},"alternative-id":["s18092751"],"URL":"https:\/\/doi.org\/10.3390\/s18092751","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,8,21]]}}}