{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:06:04Z","timestamp":1787029564762,"version":"build-2736575974"},"reference-count":70,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,5,24]],"date-time":"2018-05-24T00:00:00Z","timestamp":1527120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017R1C1B5074062"],"award-info":[{"award-number":["NRF-2017R1C1B5074062"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2016M3A9E1915855"],"award-info":[{"award-number":["NRF-2016M3A9E1915855"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017R1D1A1B03028417"],"award-info":[{"award-number":["NRF-2017R1D1A1B03028417"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Autonomous landing of an unmanned aerial vehicle or a drone is a challenging problem for the robotics research community. Previous researchers have attempted to solve this problem by combining multiple sensors such as global positioning system (GPS) receivers, inertial measurement unit, and multiple camera systems. Although these approaches successfully estimate an unmanned aerial vehicle location during landing, many calibration processes are required to achieve good detection accuracy. In addition, cases where drones operate in heterogeneous areas with no GPS signal should be considered. To overcome these problems, we determined how to safely land a drone in a GPS-denied environment using our remote-marker-based tracking algorithm based on a single visible-light-camera sensor. Instead of using hand-crafted features, our algorithm includes a convolutional neural network named lightDenseYOLO to extract trained features from an input image to predict a marker\u2019s location by visible light camera sensor on drone. Experimental results show that our method significantly outperforms state-of-the-art object trackers both using and not using convolutional neural network in terms of both accuracy and processing time.<\/jats:p>","DOI":"10.3390\/s18061703","type":"journal-article","created":{"date-parts":[[2018,5,28]],"date-time":"2018-05-28T03:54:21Z","timestamp":1527479661000},"page":"1703","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["LightDenseYOLO: A Fast and Accurate Marker Tracker for Autonomous UAV Landing by Visible Light Camera Sensor on Drone"],"prefix":"10.3390","volume":"18","author":[{"given":"Phong Ha","family":"Nguyen","sequence":"first","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1868-5207","authenticated-orcid":false,"given":"Muhammad","family":"Arsalan","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ja Hyung","family":"Koo","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rizwan Ali","family":"Naqvi","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noi Quang","family":"Truong","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang Ryoung","family":"Park","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,24]]},"reference":[{"key":"ref_1","unstructured":"(2018, January 08). The First Autonomous Drone Delivery Network will fly above Switzerland Starting next Month. Available online: https:\/\/www.theverge.com\/2017\/9\/20\/16325084\/matternet-autonomous-drone-network-switzerland."},{"key":"ref_2","unstructured":"(2018, January 08). Amazon Prime Air. Available online: https:\/\/www.amazon.com\/Amazon-Prime-Air\/b?ie=UTF8&node=8037720011."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Nguyen, P.H., Kim, K.W., Lee, Y.W., and Park, K.R. (2017). Remote marker-based tracking for UAV landing using visible-light camera sensor. Sensors, 17.","DOI":"10.3390\/s17091987"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Amer, K., Samy, M., ElHakim, R., Shaker, M., and ElHelw, M. (2017, January 22\u201329). Convolutional neural network-based deep urban signatures with application to drone localization. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.250"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, Y., Aggarwal, P., Choi, J., and Kuo, C.-C.J. (arXiv, 2017). A deep learning approach to drone monitoring, arXiv.","DOI":"10.1109\/APSIPA.2017.8282120"},{"key":"ref_7","unstructured":"Saqib, M., Khan, S.D., Sharma, N., and Blumenstein, M. (September, January 29). A study on detecting drones using deep convolutional neural networks. Proceedings of the 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, Lecce, Italy."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/LGRS.2016.2624820","article-title":"Drone classification using convolutional neural networks with merged doppler images","volume":"14","author":"Kim","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","unstructured":"Tzelepi, M., and Tefas, A. (September, January 28). Human crowd detection for drone flight safety using convolutional neural networks. Proceedings of the 25th European Signal Processing Conference, Kos Island, Greece."},{"key":"ref_10","unstructured":"Burman, P. (2016). Quadcopter Stabilization with Neural Network. [Master\u2018s Thesis, The University of Texas at Austin]."},{"key":"ref_11","unstructured":"Greenwood, D. (November, January 29). The application of neural networks to drone control. Proceedings of the International Telemetering Conference, Las Vegas, NV, USA."},{"key":"ref_12","unstructured":"Kim, D.K., and Chen, T. (arXiv, 2015). Deep neural network for real-time autonomous indoor navigation, arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Andersson, O., Wzorek, M., and Doherty, P. (2017, January 4\u201310). Deep learning quadcopter control via risk-aware active learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11041"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gandhi, D., Pinto, L., and Gupta, A. (2017, January 24\u201328). Learning to fly by crashing. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206247"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/LRA.2015.2509024","article-title":"A machine learning approach to visual perception of forest trails for mobile robots","volume":"1","author":"Giusti","year":"2016","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Smolyanskiy, N., Kamenev, A., Smith, J., and Birchfield, S. (arXiv, 2017). Toward low-flying autonomous MAV trail navigation using deep neural networks for environmental awareness, arXiv.","DOI":"10.1109\/IROS.2017.8206285"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Radovic, M., Adarkwa, O., and Wang, Q. (2017). Object recognition in aerial images using convolutional neural networks. J. Imaging, 3.","DOI":"10.3390\/jimaging3020021"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lee, J., Wang, J., Crandall, D., \u0160abanovi\u0107, S., and Fox, G. (2017, January 10\u201312). Real-time, cloud-based object detection for unmanned aerial vehicles. Proceedings of the First IEEE International Conference on Robotic Computing, Taichung, Taiwan.","DOI":"10.1109\/IRC.2017.77"},{"key":"ref_19","unstructured":"(2018, January 08). Snapdragon 835 Mobile Hardware Development Kit. Available online: https:\/\/developer.qualcomm.com\/hardware\/snapdragon-835-hdk."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhou, D., Zhong, Z., Zhang, D., Shen, L., and Yan, C. (2014, January 19\u201321). Autonomous landing of a helicopter UAV with a ground-based multisensory fusion system. Proceedings of the Seventh International Conference on Machine Vision, Milan, Italy.","DOI":"10.1117\/12.2183270"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5772\/62027","article-title":"Ground stereo vision-based navigation for autonomous take-off and landing of UAVs: A Chan-Vese model approach","volume":"13","author":"Tang","year":"2016","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/83.902291","article-title":"Active contours without edges","volume":"10","author":"Chan","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A new approach to linear filtering and prediction problems","volume":"82","author":"Kalman","year":"1960","journal-title":"J. Basic Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yang, T., Li, G., Li, J., Zhang, Y., Zhang, X., Zhang, Z., and Li, Z. (2016). A ground-based near infrared camera array system for UAV auto-landing in GPS-denied environment. Sensors, 16.","DOI":"10.3390\/s16091393"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Forster, C., Faessler, M., Fontana, F., Werlberger, M., and Scaramuzza, D. (2015, January 26\u201330). Continuous on-board monocular-vision-based elevation mapping applied to autonomous landing of micro aerial vehicles. Proceedings of the IEEE International Conference on Robotics and Automation, Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7138988"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s10846-013-9819-5","article-title":"Airborne vision-based navigation method for UAV accuracy landing using infrared lamps","volume":"72","author":"Gui","year":"2013","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s10514-016-9564-2","article-title":"Monocular vision-based real-time target recognition and tracking for autonomously landing an UAV in a cluttered shipboard environment","volume":"41","author":"Lin","year":"2017","journal-title":"Auton. Robots"},{"key":"ref_28","unstructured":"Lange, S., S\u00fcnderhauf, N., and Protzel, P. (2009, January 22\u201326). A vision based onboard approach for landing and position control of an autonomous multirotor UAV in GPS-denied environments. Proceedings of the IEEE International Conference on Advanced Robotics, Munich, Germany."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Polvara, R., Sharma, S., Wan, J., Manning, A., and Sutton, R. (2017, January 6\u20138). Towards autonomous landing on a moving vessel through fiducial markers. Proceedings of the European Conference on Mobile Robotics, Paris, France.","DOI":"10.1109\/ECMR.2017.8098671"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Falanga, D., Zanchettin, A., Simovic, A., Delmerico, J., and Scaramuzza, D. (2017, January 11\u201313). Vision-based autonomous quadrotor landing on a moving platform. Proceedings of the IEEE International Symposium on Safety, Security and Rescue Robotics, Shanghai, China.","DOI":"10.1109\/SSRR.2017.8088164"},{"key":"ref_31","unstructured":"(2018, March 22). AprilTag. Available online: https:\/\/april.eecs.umich.edu\/software\/apriltag.html."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, J., and Olson, E. (2016, January 9\u201314). AprilTag 2: Efficient and robust fiducial detection. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759617"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s10846-016-0399-z","article-title":"Vision based autonomous landing of multirotor UAV on moving platform","volume":"85","author":"Araar","year":"2017","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.patrec.2008.12.011","article-title":"Research on computer vision-based for UAV autonomous landing on a ship","volume":"30","author":"Xu","year":"2009","journal-title":"Pattern Recognit. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1016\/j.cja.2013.07.049","article-title":"Use of land\u2019s cooperative object to estimate UAV\u2019s pose for autonomous landing","volume":"26","author":"Xu","year":"2013","journal-title":"Chin. J. Aeronaut."},{"key":"ref_36","unstructured":"Polvara, R., Patacchiola, M., Sharma, S., Wan, J., Manning, A., Sutton, R., and Cangelosi, A. (arXiv, 2017). Autonomous quadrotor landing using deep reinforcement learning, arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","unstructured":"(2018, March 26). Dongguk Drone Camera Database (DDroneC\u2013DB2) & lightDenseYOLO. Available online: http:\/\/dm.dgu.edu\/link.html."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_40","unstructured":"Welch, G., and Bishop, G. (2001, January 12\u201317). An introduction to the Kalman filter. Proceedings of the Special Interest Group on GRAPHics and Interactive Techniques, Los Angeles, CA, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_43","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the International Conference on Neural Information Processing Systems 25, Lake Tahoe, NV, USA."},{"key":"ref_44","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_46","unstructured":"(2018, February 08). OpenCV Image Thresholding. Available online: https:\/\/docs.opencv.org\/3.4.0\/d7\/d4d\/tutorial_py_thresholding.html."},{"key":"ref_47","unstructured":"(2018, February 08). OpenCV Morphological Transformations. Available online: https:\/\/docs.opencv.org\/trunk\/d9\/d61\/tutorial_py_morphological_ops.html."},{"key":"ref_48","unstructured":"(2018, February 08). Phantom 4. Available online: https:\/\/www.dji.com\/phantom-4."},{"key":"ref_49","unstructured":"(2018, February 08). GeForce GTX 1070. Available online: https:\/\/www.geforce.com\/hardware\/desktop-gpus\/geforce-gtx-1070\/specifications."},{"key":"ref_50","unstructured":"(2018, February 08). Qualcomm Snapdragon Neural Processing Engine for AI. Available online: https:\/\/developer.qualcomm.com\/software\/snapdragon-neural-processing-engine-ai."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (arXiv, 2014). Caffe: Convolutional architecture for fast feature embedding, arXiv.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_52","unstructured":"(2018, February 08). Caffe2. Available online: http:\/\/caffe2.ai\/."},{"key":"ref_53","unstructured":"(2018, February 08). TensorFlow. Available online: https:\/\/www.tensorflow.org\/."},{"key":"ref_54","unstructured":"(2018, April 21). Stanford Drone Dataset. Available online: http:\/\/cvgl.stanford.edu\/projects\/uav_data\/."},{"key":"ref_55","unstructured":"(2017, May 19). Mini\u2013Drone Video Dataset. Available online: http:\/\/mmspg.epfl.ch\/mini-drone."},{"key":"ref_56","unstructured":"(2017, May 19). SenseFly Dataset. Available online: https:\/\/www.sensefly.com\/education\/datasets\/."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The Pascal visual object classes challenge: A retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 8\u201316). SSD: Single shot multibox detector. Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_60","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, Sardinia, Italy."},{"key":"ref_61","unstructured":"(2018, February 09). Darknet: Open Source Neural Networks in C. Available online: https:\/\/pjreddie.com\/darknet\/."},{"key":"ref_62","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (arXiv, 2017). MobileNets: Efficient convolutional neural networks for mobile vision applications, arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., and Guadarrama, S. (2017, January 21\u201326). Speed\/accuracy trade-offs for modern convolutional object detectors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.351"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Hosang, J., Benenson, R., and Schiele, B. (arXiv, 2014). How good are detection proposals, really?, arXiv.","DOI":"10.5244\/C.28.24"},{"key":"ref_65","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_66","doi-asserted-by":"crossref","unstructured":"Chavali, N., Agrawal, H., Mahendru, A., and Batra, D. (2016, January 27\u201330). Object-proposal evaluation protocol is \u2018gameable\u2019. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.97"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Babenko, B., Yang, M.H., and Belongie, S. (2009, January 20\u201326). Visual tracking with online multiple instance learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206737"},{"key":"ref_68","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_69","doi-asserted-by":"crossref","unstructured":"Kalal, Z., Mikolajczyk, K., and Matas, J. (2010, January 23\u201326). Forward\u2013backward error: Automatic detection of tracking failures. Proceedings of the International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.675"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","article-title":"High\u2013speed tracking with kernelized correlation filters","volume":"37","author":"Henriques","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/6\/1703\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:05:49Z","timestamp":1760195149000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/6\/1703"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,24]]},"references-count":70,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["s18061703"],"URL":"https:\/\/doi.org\/10.3390\/s18061703","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,24]]}}}