{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T23:22:56Z","timestamp":1751757776756,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030711863"},{"type":"electronic","value":"9783030711870"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-71187-0_40","type":"book-chapter","created":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T05:04:54Z","timestamp":1622610294000},"page":"435-445","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["In-Car State Classification with RGB Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4590-3727","authenticated-orcid":false,"given":"Pedro","family":"Faria","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4595-3828","authenticated-orcid":false,"given":"Sandra","family":"Dixe","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1452-7842","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Leite","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7002-8496","authenticated-orcid":false,"given":"Sahar","family":"Azadi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3317-8238","authenticated-orcid":false,"given":"Jos\u00e9","family":"Mendes","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6703-3278","authenticated-orcid":false,"given":"Jaime C.","family":"Fonseca","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5880-033X","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Borges","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,3]]},"reference":[{"issue":"January","key":"40_CR1","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.trc.2019.12.008","volume":"111","author":"S Narayanan","year":"2020","unstructured":"Narayanan, S., Chaniotakis, E., Antoniou, C.: Shared autonomous vehicle services: a comprehensive review. Transp. Res. Part C Emerg. Technol. 111(January), 255\u2013293 (2020)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"issue":"1","key":"40_CR2","first-page":"47","volume":"5","author":"M Hao","year":"2018","unstructured":"Hao, M., Yamamoto, T.: Shared autonomous vehicles: a review considering car sharing and autonomous vehicles. Asian Transp. Stud. 5(1), 47\u201363 (2018)","journal-title":"Asian Transp. Stud."},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Torres, H.R., et al.: Real-time human body pose estimation for in-car depth images. In: IFIP Advances in Information and Communication Technology, vol. 553, pp. 169\u2013182. Springer, New York LLC (2019)","DOI":"10.1007\/978-3-030-17771-3_14"},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Borges, J., et al.: Automated generation of synthetic in-car dataset for human body pose detection. In: VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, vol. 5, no. Visigrapp, pp. 550\u2013557 (2020)","DOI":"10.5220\/0009316205500557"},{"issue":"1","key":"40_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-020-01131-z","volume":"32","author":"J Borges","year":"2021","unstructured":"Borges, J., et al.: A system for the generation of in-car human body pose datasets. Mach. Vis. Appl. 32(1), 1\u201315 (2021)","journal-title":"Mach. Vis. Appl."},{"issue":"5","key":"40_CR6","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.infrared.2010.06.002","volume":"53","author":"J Liu","year":"2010","unstructured":"Liu, J., Yang, W., Dai, J.: Research on thermal wave processing of lock-in thermography based on analyzing image sequences for NDT. Infrared Phys. Technol. 53(5), 348\u2013357 (2010)","journal-title":"Infrared Phys. Technol."},{"issue":"1","key":"40_CR7","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1080\/00405000.2012.692940","volume":"104","author":"J Jing","year":"2013","unstructured":"Jing, J., Zhang, H., Wang, J., Li, P., Jia, J.: Fabric defect detection using Gabor filters and defect classification based on LBP and Tamura method. J. Text. Inst. 104(1), 18\u201327 (2013)","journal-title":"J. Text. Inst."},{"key":"40_CR8","doi-asserted-by":"crossref","unstructured":"Hu, G.-H.: Optimal ring Gabor filter design for texture defect detection using a simulated annealing algorithm. In: Proceedings of the 2014 International Conference of Information Science, Electronic and Electrical Engineering, ISEEE 2014, vol. 2, pp. 860\u2013864 (2014)","DOI":"10.1109\/InfoSEEE.2014.6947789"},{"key":"40_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/07391102.2020.1788642","volume":"38","author":"A Jaiswal","year":"2020","unstructured":"Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., Kaur, M.: Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. J. Biomol. Struct. Dyn. 38, 1\u20138 (2020)","journal-title":"J. Biomol. Struct. Dyn."},{"issue":"7","key":"40_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/biom10070984","volume":"10","author":"S Sukegawa","year":"2020","unstructured":"Sukegawa, S., et al.: Deep neural networks for dental implant system classification. Biomolecules 10(7), 1\u201313 (2020)","journal-title":"Biomolecules"},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Wu, Y., Qin, X., Pan, Y., Yuan, C.: Convolution neural network based transfer learning for classification of flowers. In: 2018 IEEE 3rd International Conference on Signal Image Process. ICSIP 2018, pp. 562\u2013566 (2019)","DOI":"10.1109\/SIPROCESS.2018.8600536"},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning, Lecture Notes in Computer Science (including Subseries of Lecture Notes Artificial Intelligence and Lecture Notes Bioinformatics), vol. 11141, LNCS, pp. 270\u2013279 (2018)","DOI":"10.1007\/978-3-030-01424-7_27"},{"issue":"9","key":"40_CR13","doi-asserted-by":"publisher","first-page":"12421","DOI":"10.1007\/s11042-018-6786-7","volume":"78","author":"L Liu","year":"2019","unstructured":"Liu, L., Zhang, J., Fu, X., Liu, L., Huang, Q.: Unsupervised segmentation and elm for fabric defect image classification. Multimed. Tools Appl. 78(9), 12421\u201312449 (2019)","journal-title":"Multimed. Tools Appl."},{"issue":"4","key":"40_CR14","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1108\/IJCST-11-2018-0135","volume":"31","author":"PR Jeyaraj","year":"2019","unstructured":"Jeyaraj, P.R., Samuel Nadar, E.R.: Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm. Int. J. Cloth. Sci. Technol. 31(4), 510\u2013521 (2019)","journal-title":"Int. J. Cloth. Sci. Technol."},{"key":"40_CR15","unstructured":"Universitesi, C.: Evaluation of Fabric Defect Detection Based on Transfer Learning with Pre-trained AlexNet Onceden E gitilmis AlexNet ile Transfer O Dayal\u0131 Kumas Hata Tespitinin De gerlendirilmesi (2018)"},{"key":"40_CR16","doi-asserted-by":"publisher","unstructured":"Patil, K., Kulkarni, M., Sriraman, A., Karande, S.: Deep learning based car damage classification. In: Proceedings - 16th IEEE International Conference on Machine Learning and Applications. ICMLA 2017, vol. 2017, pp. 50\u201354 (2017). https:\/\/doi.org\/10.1109\/ICMLA.2017.0-179","DOI":"10.1109\/ICMLA.2017.0-179"},{"key":"40_CR17","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"40_CR18","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and<0.5MB model size, pp. 1\u201313 (2016)"},{"key":"40_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 07, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"40_CR20","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, pp. 1\u201314 (2015)"},{"key":"40_CR21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"40_CR22","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"40_CR23","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings - 30th IEEE Conference on Computer Vision Pattern Recognition, CVPR 2017, vol. 2017, pp. 6517\u20136525 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"40_CR24","unstructured":"Howard, A.G., et al.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017)"},{"key":"40_CR25","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017, pp. 1800\u20131807 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"40_CR26","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520. IEEE","DOI":"10.1109\/CVPR.2018.00474"},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017, pp. 2261\u20132269 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"40_CR28","unstructured":"Adam, G., Lorraine, J.: Understanding Neural Architecture Search Techniques (2019)"},{"key":"40_CR29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"40_CR30","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: AAAI, vol. 4, p. 12 (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"40_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X, Lin, M., Sun, J.:. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. arXiv preprint arXiv:1707.01083v2 (2017)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"40_CR32","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., Le, Q.W.: Learning Transferable Architectures for Scalable Image Recognition. 2, no. 6. arXiv preprint arXiv:1707.07012 (2017)","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Advances in Intelligent Systems and Computing","Intelligent Systems Design and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-71187-0_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T10:52:02Z","timestamp":1672311122000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-71187-0_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030711863","9783030711870"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-71187-0_40","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"3 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems Design and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isda2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mirlabs.net\/isda20\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}