{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:46:06Z","timestamp":1760240766397,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T00:00:00Z","timestamp":1568160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Kernel correlation filters (KCF) demonstrate significant potential in visual object tracking by employing robust descriptors. Proper selection of color and texture features can provide robustness against appearance variations. However, the use of multiple descriptors would lead to a considerable feature dimension. In this paper, we propose a novel low-rank descriptor, that provides better precision and success rate in comparison to state-of-the-art trackers. We accomplished this by concatenating the magnitude component of the Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP), Robustness-Driven Hybrid Descriptor (RDHD), Histogram of Oriented Gradients (HoG), and Color Naming (CN) features. We reduced the rank of our proposed multi-channel feature to diminish the computational complexity. We formulated the Support Vector Machine (SVM) model by utilizing the circulant matrix of our proposed feature vector in the kernel correlation filter. The use of discrete Fourier transform in the iterative learning of SVM reduced the computational complexity of our proposed visual tracking algorithm. Extensive experimental results on Visual Tracker Benchmark dataset show better accuracy in comparison to other state-of-the-art trackers.<\/jats:p>","DOI":"10.3390\/sym11091155","type":"journal-article","created":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T11:26:34Z","timestamp":1568201194000},"page":"1155","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Low-Rank Multi-Channel Features for Robust Visual Object Tracking"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3860-2635","authenticated-orcid":false,"family":"Fawad","sequence":"first","affiliation":[{"name":"ACTSENA Research Group, Telecommunication Engineering Department, University of Engineering and Technology Taxila, Punjab 47050, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8616-3959","authenticated-orcid":false,"given":"Muhammad","family":"Jamil Khan","sequence":"additional","affiliation":[{"name":"ACTSENA Research Group, Telecommunication Engineering Department, University of Engineering and Technology Taxila, Punjab 47050, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5084-7862","authenticated-orcid":false,"given":"MuhibUr","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4968-993X","authenticated-orcid":false,"given":"Yasar","family":"Amin","sequence":"additional","affiliation":[{"name":"ACTSENA Research Group, Telecommunication Engineering Department, University of Engineering and Technology Taxila, Punjab 47050, Pakistan"}]},{"given":"Hannu","family":"Tenhunen","sequence":"additional","affiliation":[{"name":"Department of Electronic Systems, Royal Institute of Technology (KTH), Isafjordsgatan 26, SE 16440 Stockholm, Sweden"},{"name":"Department of Information Technology, TUCS, University of Turku, 20520 Turku, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.patrec.2014.04.011","article-title":"Human activity recognition from 3d data: A review","volume":"48","author":"Aggarwal","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"016014","DOI":"10.1117\/1.JRS.12.016014","article-title":"Multiple vehicle tracking in aerial video sequence using driver behavior analysis and improved deterministic data association","volume":"12","author":"Zhang","year":"2018","journal-title":"J. Appl. Remote. Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17654\/EC016010001","article-title":"Object tracking algorithm implementation for security applications","volume":"16","author":"Sivanantham","year":"2016","journal-title":"Far East J. Electron. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yun, X., Sun, Y., Yang, X., and Lu, N. (2019). Discriminative Fusion Correlation Learning for Visible and Infrared Tracking. Math. Probl. Eng.","DOI":"10.1155\/2019\/2437521"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.patcog.2017.11.007","article-title":"Deep visual tracking: Review and experimental comparison","volume":"76","author":"Li","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.cosrev.2018.03.001","article-title":"New trends on moving object detection in video images captured by a moving camera: A survey","volume":"28","author":"Yazdi","year":"2018","journal-title":"Comput. Sci. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"16989","DOI":"10.1007\/s11042-016-3647-0","article-title":"A review of visual moving target tracking","volume":"76","author":"Pan","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_8","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 IEEE conference on computer vision and pattern recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.312"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2777","DOI":"10.1109\/TIP.2018.2813161","article-title":"Robust visual tracking revisited: From correlation filter to template matching","volume":"27","author":"Liu","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11263-007-0075-7","article-title":"Incremental learning for robust visual tracking","volume":"77","author":"Ross","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1109\/TPAMI.2015.2509974","article-title":"Struck: Structured output tracking with kernels","volume":"38","author":"Hare","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zhang, L., Liu, Q., Zhang, D., and Yang, M.H. (2014, January 6\u201312). Fast visual tracking via dense spatio-temporal context learning. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_9"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1109\/TPAMI.2018.2829180","article-title":"Learning support correlation filters for visual tracking","volume":"41","author":"Zuo","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2137","DOI":"10.1109\/TPAMI.2016.2516982","article-title":"A novel performance evaluation methodology for single-target trackers","volume":"38","author":"Kristan","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1109\/TPAMI.2015.2417577","article-title":"Nus-pro: A new visual tracking challenge","volume":"38","author":"Li","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fan, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., and Ling, H. (2019, January 16\u201320). Lasot: A high-quality benchmark for large-scale single object tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00552"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.patcog.2019.02.004","article-title":"A labeled random finite set online multi-object tracker for video data","volume":"90","author":"Kim","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Babenko, B., Yang, M.H., and Belongie, S. (2009, January 20\u201325). Visual tracking with online multiple instance learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPRW.2009.5206737"},{"key":"ref_19","first-page":"6","article-title":"Real-time tracking via on-line boosting","volume":"1","author":"Grabner","year":"2006","journal-title":"Bmvc"},{"key":"ref_20","unstructured":"Nam, H., and Han, B. (1, January 26). Learning multi-domain convolutional neural networks for visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"2356","DOI":"10.1109\/TIP.2014.2313227","article-title":"Robust object tracking via sparse collaborative appearance model","volume":"23","author":"Zhong","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1109\/TIP.2017.2777183","article-title":"Learning common and feature-specific patterns: A novel multiple-sparse-representation-based tracker","volume":"27","author":"Lan","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","unstructured":"Zhong, W., Lu, H., and Yang, M.-H. (2012, January 16\u201321). Robust object tracking via sparsity based collaborative model. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_25","unstructured":"Jia, X., Lu, H., and Yang, M.H. (2012, January 16\u201321). Visual tracking via adaptive structural local sparse appearance model. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"ref_26","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.","DOI":"10.1109\/CVPR.2015.7298675"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ma, S., and Sclaroff, S. (2014, January 6\u201312). MEEM: Robust tracking via multiple experts using entropy minimization. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10599-4_13"},{"key":"ref_28","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 Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1109\/TCYB.2017.2675910","article-title":"Constrained superpixel tracking","volume":"48","author":"Wang","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1109\/TCYB.2017.2716101","article-title":"Deformable parts correlation filters for robust visual tracking","volume":"48","author":"Lukezic","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Henriques, J.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, Florence, Italy.","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Montero, A.S., Lang, J., and Laganiere, R. (2015, January 7\u201313). Scalable kernel correlation filter with sparse feature integration. Proceedings of the IEEE International Conference on Computer Vision Workshop (ICCVW), Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.80"},{"key":"ref_34","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 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299094"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Khan, F.S., and Felsberg, M. (2015, January 7\u201313). Learning spatially regularized correlation filters for visual tracking. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.490"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bibi, A., Mueller, M., and Ghanem, B. (2016, January 11\u201314). Target response adaptation for correlation filter tracking. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46466-4_25"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5596","DOI":"10.1109\/TIP.2019.2919201","article-title":"Learning Adaptive Discriminative Correlation Filters via Temporal Consistency preserving Spatial Feature Selection for Robust Visual Object Tracking","volume":"28","author":"Xu","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","first-page":"6309","article-title":"Discriminative correlation filter with channel and spatial reliability","volume":"126","author":"Lukei","year":"2017","journal-title":"CVPR"},{"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":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","unstructured":"Danelljan, M., Hager, G., Khan, F.S., and Felsberg, M. (July, January 26). Adaptive decontamination of the training set: A unified formulation for discriminative visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tu, Z., Guo, L., Li, C., Xiong, Z., and Wang, X. (2018). Minimum Barrier Distance-Based Object Descriptor for Visual Tracking. Appl. Sci., 8.","DOI":"10.3390\/app8112233"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object detection with discriminatively trained part-based models","volume":"32","author":"Felzenszwalb","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Khan, F.S., Felsberg, M., and Weijer, J.V.D. (2014, January 23\u201328). Adaptive color attributes for real-time visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.143"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Possegger, H., Mauthner, T., and Bischof, H. (2015, January 7\u201312). In defense of color-based model-free tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298823"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lukezic, A., Vojir, T., Zajc, L.C., Matas, J., and Kristan, M. (2017, January 21\u201326). Discriminative correlation filter with channel and spatial reliability. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.515"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1109\/TCSVT.2017.2671899","article-title":"Letrist: Locally encoded transform feature histogram for rotation-invariant texture classification","volume":"28","author":"Song","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Techol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"110116","DOI":"10.1109\/ACCESS.2019.2932687","article-title":"Robustness-Driven Hybrid Descriptor for Noise-Deterrent Texture Classification","volume":"7","author":"Saeed","year":"2019","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"66668","DOI":"10.1109\/ACCESS.2019.2918004","article-title":"Texture Representation through Overlapped Multi-oriented Tri-scale Local Binary Pattern","volume":"7","author":"Khan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1049\/iet-ipr.2017.0368","article-title":"License number plate recognition system using entropy-based features selection approach with SVM","volume":"12","author":"Khan","year":"2017","journal-title":"IET Image Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.patcog.2016.08.006","article-title":"Combining local and global: Rich and robust feature pooling for visual recognition","volume":"62","author":"Xiong","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3102","DOI":"10.1016\/j.patcog.2014.12.016","article-title":"Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding","volume":"48","author":"Zhang","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Arsalan, M., Hong, H., Naqvi, R., Lee, M., Kim, M.D., and Park, K. (2017). Deep learning-based iris segmentation for iris recognition in visible light environment. Symmetry, 9.","DOI":"10.3390\/sym9110263"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Masood, H., Rehman, S., Khan, A., Riaz, F., Hassan, A., and Abbas, M. (2019). Approximate Proximal Gradient-Based Correlation Filter for Target Tracking in Videos: A Unified Approach. Arab. J. Sci. Eng., 1\u201318.","DOI":"10.1007\/s13369-019-03861-3"},{"key":"ref_54","unstructured":"Qi, Y., Zhang, S., Qin, L., Yao, H., Huang, Q., Lim, J., and Yang, M.H. (July, January 26). Hedged deep tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Hare, S., Saffari, A., and Struck, P.H.T. (2011, January 6\u201313). Structured output tracking with kernels. Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126251"},{"key":"ref_56","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_57","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zhang, L., and Yang, M.H. (2012, January 7\u201313). Real-time compressive tracking. Proceedings of the European Conference on Computer Vision, Florence, Italy.","DOI":"10.1007\/978-3-642-33712-3_62"},{"key":"ref_58","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 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299177"},{"key":"ref_59","unstructured":"Bao, C., Wu, Y., Ling, H., and Ji, H. (2012, January 16\u201321). Real time robust l1 tracker using accelerated proximal gradient approach. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_60","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_61","doi-asserted-by":"crossref","unstructured":"Dinh, T.B., Vo, N., and Medioni, G. (2011). Context tracker: Exploring supporters and distracters in unconstrained environments. CVPR, 1177\u20131184.","DOI":"10.1109\/CVPR.2011.5995733"},{"key":"ref_62","unstructured":"Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., and Torr, P.H. (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_63","doi-asserted-by":"crossref","unstructured":"Gao, J., Ling, H., Hu, W., and Xing, J. (2014, January 6\u201312). Transfer learning based visual tracking with gaussian processes regression. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10578-9_13"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/9\/1155\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:18:51Z","timestamp":1760188731000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/9\/1155"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,11]]},"references-count":63,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["sym11091155"],"URL":"https:\/\/doi.org\/10.3390\/sym11091155","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2019,9,11]]}}}