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Project of Shaanxi Province","award":["12005169"],"award-info":[{"award-number":["12005169"]}]},{"name":"Philosophy and Social Science Research Project of Shaanxi Province","award":["2021JC-23"],"award-info":[{"award-number":["2021JC-23"]}]},{"name":"Philosophy and Social Science Research Project of Shaanxi Province","award":["CXY 2020-094"],"award-info":[{"award-number":["CXY 2020-094"]}]},{"name":"Philosophy and Social Science Research Project of Shaanxi Province","award":["SXLK2022-02-8"],"award-info":[{"award-number":["SXLK2022-02-8"]}]},{"name":"Philosophy and Social Science Research Project of Shaanxi Province","award":["20222HZ1759"],"award-info":[{"award-number":["20222HZ1759"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Object detection and tracking has always been one of the important research directions in computer vision. The purpose is to determine whether the object is contained in the input image and enclose the object with a bounding box. However, most object detection and tracking methods are applied to daytime objects, and the processing of nighttime objects is imprecise. In this paper, a spectral-spatial feature enhancement algorithm for nighttime object detection and tracking is proposed, which is inspired by symmetrical neural networks. The proposed method consists of the following steps. First, preprocessing is performed on unlabeled nighttime images, including low-light enhancement, object detection, and dynamic programming. Second, object features for daytime and nighttime times are extracted and modulated with a domain-adaptive structure. Third, the Siamese network can make full use of daytime and nighttime object features, which is trained as a tracker by the above images. Fourth, the test set is subjected to feature enhancement and then input to the tracker to obtain the final detection and tracking results. The feature enhancement step includes low-light enhancement and Gabor filtering. The spatial-spectral features of the target are fully extracted in this step. The NAT2021 dataset is used in the experiments. Six methods are employed as comparisons. Multiple judgment indicators were used to analyze the research results. The experimental results show that the method achieves excellent detection and tracking performance.<\/jats:p>","DOI":"10.3390\/sym15020546","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T04:58:23Z","timestamp":1676869103000},"page":"546","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Spectral-Spatial Feature Enhancement Algorithm for Nighttime Object Detection and Tracking"],"prefix":"10.3390","volume":"15","author":[{"given":"Yan","family":"Lv","sequence":"first","affiliation":[{"name":"The Optoelectronic Information Department, School of Optoelectronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Feng","sequence":"additional","affiliation":[{"name":"The Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Wang","sequence":"additional","affiliation":[{"name":"The Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0677-6702","authenticated-orcid":false,"given":"Gabriel","family":"Dauphin","sequence":"additional","affiliation":[{"name":"The Laboratory of Information Processing and Transmission, L2TI, Institut Galil\u00e9e, University Paris XIII, 75013 Villetaneuse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yali","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengdao","family":"Xing","sequence":"additional","affiliation":[{"name":"The Academy of Advanced Interdisciplinary Research, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,17]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Improved Kernel Correlation Filter Based Moving Target Tracking for Robot Grasping","volume":"71","author":"Peng","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, C., Ibrayim, M., and Hamdulla, A. (2022). Multi-Feature Single Target Robust Tracking Fused with Particle Filter. Sensors, 22.","DOI":"10.3390\/s22051879"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1109\/TIP.2020.3043113","article-title":"Bio-inspired video enhancement for small moving target detection","volume":"30","author":"Uzair","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Abro, G.E.M., Zulkifli, S.A.B.M., Masood, R.J., Asirvadam, V.S., and Laouti, A. (2022). Comprehensive Review of UAV Detection, Security, and Communication Advancements to Prevent Threats. Drones, 6.","DOI":"10.3390\/drones6100284"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/s11263-020-01387-y","article-title":"Lasot: A high-quality large-scale single object tracking benchmark","volume":"129","author":"Fan","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1109\/TPAMI.2019.2957464","article-title":"Got-10k: A large high-diversity benchmark for generic object tracking in the wild","volume":"43","author":"Huang","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Real, E., Shlens, J., Mazzocchi, S., Pan, X., and Vanhoucke, V. (2017, January 21\u201326). Youtube-boundingboxes: A large high-precision human-annotated data set for object detection in video. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.789"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.1109\/JSEN.2014.2332098","article-title":"Target tracking using machine learning and Kalman filter in wireless sensor networks","volume":"14","author":"Mahfouz","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.1109\/TSMCB.2012.2236647","article-title":"Distributed optimal consensus filter for target tracking in heterogeneous sensor networks","volume":"43","author":"Zhu","year":"2013","journal-title":"IEEE Trans. Cybern."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/TAES.2007.4383605","article-title":"Iterated unscented Kalman filter for passive target tracking","volume":"43","author":"Zhan","year":"2007","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hao, J., Zhou, Y., Zhang, G., Lv, Q., and Wu, Q. (2018, January 25\u201327). A review of target tracking algorithm based on UAV. Proceedings of the 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), Shenzhen, China.","DOI":"10.1109\/CBS.2018.8612263"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3330427","DOI":"10.1155\/2022\/3330427","article-title":"Research and Implementation of Robot Vision Scanning Tracking Algorithm Based on Deep Learning","volume":"2022","author":"Guo","year":"2022","journal-title":"Scanning"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2697","DOI":"10.3233\/JIFS-189312","article-title":"Machine learning model for feature recognition of sports competition based on improved TLD algorithm","volume":"40","author":"Ding","year":"2021","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hossain, S., and Lee, D.j. (2019). Deep learning-based real-time multiple-object detection and tracking from aerial imagery via a flying robot with GPU-based embedded devices. Sensors, 19.","DOI":"10.3390\/s19153371"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Leclerc, M., Tharmarasa, R., Florea, M.C., Boury-Brisset, A.C., Kirubarajan, T., and Duclos-Hindi\u00e9, N. (2018, January 10\u201313). Ship classification using deep learning techniques for maritime target tracking. Proceedings of the 2018 21st International Conference on Information Fusion (FUSION), Cambridge, UK.","DOI":"10.23919\/ICIF.2018.8455679"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9878","DOI":"10.1109\/JIOT.2020.3016694","article-title":"Offloading optimization in edge computing for deep-learning-enabled target tracking by internet of UAVs","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Peng, Y., Tang, Z., Zhao, G., Cao, G., and Wu, C. (2021). Motion Blur Removal for Uav-Based Wind Turbine Blade Images Using Synthetic Datasets. Remote Sens., 14.","DOI":"10.3390\/rs14010087"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cao, Z., Fu, C., Ye, J., Li, B., and Li, Y. (2021, January 11\u201317). HiFT: Hierarchical feature transformer for aerial tracking. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.01517"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhong, B., Li, G., Zhang, S., and Ji, R. (2020, January 13\u201319). Siamese box adaptive network for visual tracking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00670"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhao, B., Gong, X., Wang, J., and Zhao, L. (2021). Low-Light Image Enhancement Based on Multi-Path Interaction. Sensors, 21.","DOI":"10.3390\/s21154986"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Feng, W., Quan, Y., and Dauphin, G. (2020). Label noise cleaning with an adaptive ensemble method based on noise detection metric. Sensors, 20.","DOI":"10.3390\/s20236718"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3866","DOI":"10.1109\/LRA.2022.3146911","article-title":"Tracker Meets Night: A Transformer Enhancer for UAV Tracking","volume":"7","author":"Ye","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ye, J., Fu, C., Zheng, G., Cao, Z., and Li, B. (October, January 27). DarkLighter: Light up the darkness for UAV tracking. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636680"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rakhmatulin, I., Kamilaris, A., and Andreasen, C. (2021). Deep neural networks to detect weeds from crops in agricultural environments in real-time: A review. Remote Sens., 13.","DOI":"10.2139\/ssrn.3959386"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhu, H., Wei, H., Li, B., Yuan, X., and Kehtarnavaz, N. (2020). A Review of Video Object Detection: Datasets, Metrics and Methods. Appl. Sci., 10.","DOI":"10.3390\/app10217834"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yang, L., Liu, S., and Zhao, Y. (2022). Deep-Learning Based Algorithm for Detecting Targets in Infrared Images. Appl. Sci., 12.","DOI":"10.3390\/app12073322"},{"key":"ref_27","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_28","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_29","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1109\/TPAMI.2011.231","article-title":"CPMC: Automatic object segmentation using constrained parametric min-cuts","volume":"34","author":"Carreira","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Van de Sande, K.E., Uijlings, J.R., Gevers, T., and Smeulders, A.W. (2011, January 6\u201313). Segmentation as selective search for object recognition. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126456"},{"key":"ref_31","first-page":"128","article-title":"Multiscale combinatorial grouping for image segmentation and object proposal generation","volume":"39","author":"Arbelaez","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","unstructured":"Wang Lin, L., Liu, S., and Chen, Y.W. (2018). Method and Apparatus of Candidate Generation for Single Sample Mode in Video Coding. (10,021,418), US Patent."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104845","DOI":"10.1016\/j.knosys.2019.07.016","article-title":"New margin-based subsampling iterative technique in modified random forests for classification","volume":"182","author":"Feng","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_37","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.ins.2021.06.059","article-title":"Semi-supervised rotation forest based on ensemble margin theory for the classification of hyperspectral image with limited training data","volume":"575","author":"Feng","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"55546","DOI":"10.1109\/ACCESS.2022.3177628","article-title":"YOLO-G: A Lightweight Network Model for Improving the Performance of Military Targets Detection","volume":"10","author":"Kong","year":"2022","journal-title":"IEEE Access"},{"key":"ref_41","first-page":"992","article-title":"Indoor target tracking with deep learning-based YOLOv3 model","volume":"Volume 12342","author":"Dong","year":"2022","journal-title":"Proceedings of the Fourteenth International Conference on Digital Image Processing (ICDIP 2022)"},{"key":"ref_42","unstructured":"Jiang, S., Xu, B., Zhao, J., and Shen, F. (2021). Faster and simpler siamese network for single object tracking. arXiv."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., and Torr, P.H. (2016, January 11\u201314). Fully-convolutional siamese networks for object tracking. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"ref_45","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00441"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Guo, D., Wang, J., Cui, Y., Wang, Z., and Chen, S. (2020, January 14\u201319). SiamCAR: Siamese fully convolutional classification and regression for visual tracking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00630"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wang, Z., Li, Z., Yuan, Y., and Yu, G. (2020, January 7\u20138). Siamfc++: Towards robust and accurate visual tracking with target estimation guidelines. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6944"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chen, X., Yan, B., Zhu, J., Wang, D., Yang, X., and Lu, H. (2021, January 20\u201325). Transformer Tracking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00803"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, N., Zhou, W., Wang, J., and Li, H. (2021, January 20\u201325). Transformer meets tracker: Exploiting temporal context for robust visual tracking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00162"},{"key":"ref_50","first-page":"1167","article-title":"Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation","volume":"34","author":"Liu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Rakshit, S., Bandyopadhyay, H., Bharambe, P., Desetti, S.N., Banerjee, B., and Chaudhuri, S. (2022, January 18\u201324). Open-Set Domain Adaptation Under Few Source-Domain Labeled Samples. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00448"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Sakaridis, C., Dai, D., and Van Gool, L. (2018, January 18\u201322). Domain adaptive faster r-cnn for object detection in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00352"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yu, Q., Fan, K., Wang, Y., and Zheng, Y. (2022). Faster MDNet for Visual Object Tracking. Appl. Sci., 12.","DOI":"10.3390\/app12052336"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4622","DOI":"10.1109\/TIP.2022.3186537","article-title":"A Multistage Framework With Mean Subspace Computation and Recursive Feedback for Online Unsupervised Domain Adaptation","volume":"31","author":"Moon","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"104152","DOI":"10.1016\/j.autcon.2022.104152","article-title":"Single-image localisation using 3D models: Combining hierarchical edge maps and semantic segmentation for domain adaptation","volume":"136","author":"Acharya","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4818","DOI":"10.1109\/TGRS.2020.2967778","article-title":"Hyperspectral image spectral\u2013spatial-range Gabor filtering","volume":"58","author":"He","year":"2020","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, C., Guo, C., and Loy, C.C. (2021). Learning to enhance low-light image via zero-reference deep curve estimation. arXiv.","DOI":"10.1109\/TPAMI.2021.3063604"},{"key":"ref_58","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 Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zheng, J., Ma, C., Peng, H., and Yang, X. (2021, January 11\u201317). Learning to Track Objects from Unlabeled Videos. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.01329"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ye, J., Fu, C., Zheng, G., Paudel, D.P., and Chen, G. (2022, January 18\u201324). Unsupervised domain adaptation for nighttime aerial tracking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00869"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","article-title":"A survey on vision transformer","volume":"45","author":"Han","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_62","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_63","unstructured":"Ganin, Y., and Lempitsky, V. (2015, January 7\u20139). Unsupervised domain adaptation by backpropagation. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., and Paul Smolley, S. (2017, January 22\u201329). Least squares generative adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1109\/TIP.2002.804262","article-title":"Comparison of texture features based on Gabor filters","volume":"11","author":"Grigorescu","year":"2002","journal-title":"IEEE Trans. Image Process."},{"key":"ref_66","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 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_67","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized Intersection over Union. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Lukezic, A., Matas, J., and Kristan, M. (2020, January 14\u201319). D3S-A Discriminative Single Shot Segmentation Tracker. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00716"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., Bischof, H., Brox, T., and Frahm, J.M. (2020, January 23\u201328). Ocean: Object-Aware Anchor-Free Tracking. Proceedings of the Computer Vision\u2014ECCV 2020, Glasgow, UK.","DOI":"10.1007\/978-3-030-58598-3"},{"key":"ref_70","unstructured":"Zhang, L., Gonzalez-Garcia, A., Weijer, J.V.D., Danelljan, M., and Khan, F.S. (November, January 27). Learning the Model Update for Siamese Trackers. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/2\/546\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:39:52Z","timestamp":1760121592000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/2\/546"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,17]]},"references-count":70,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["sym15020546"],"URL":"https:\/\/doi.org\/10.3390\/sym15020546","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,17]]}}}