{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:10:09Z","timestamp":1743127809108,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819784899"},{"type":"electronic","value":"9789819784905"}],"license":[{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-97-8490-5_21","type":"book-chapter","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T09:13:52Z","timestamp":1730884432000},"page":"290-303","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["IMO-Net: Integrated Memory Optimization Network for Video Instance Lane Detection"],"prefix":"10.1007","author":[{"given":"Boyong","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yunfei","family":"Yin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"21_CR1","unstructured":"Tusimple: Tusimple benchmark. Accessed 1 October 2023. https:\/\/github.com\/TuSimple\/tusimple-benchmark\/"},{"key":"21_CR2","unstructured":"Cheng, H.K., Tai, Y.W., Tang, C.K.: Rethinking space-time networks with improved memory coverage for efficient video object segmentation. In: Neural Information Processing Systems (2021)"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Hou, Y., Ma, Z., Liu, C., Loy, C.C.: Learning lightweight lane detection cnns by self attention distillation. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1013\u20131021 (2019)","DOI":"10.1109\/ICCV.2019.00110"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Johnander, J., Danelljan, M., Brissman, E., Khan, F.S., Felsberg, M.: A generative appearance model for end-to-end video object segmentation. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8945\u20138954 (2018)","DOI":"10.1109\/CVPR.2019.00916"},{"key":"21_CR5","unstructured":"Ko, Y., Jun, J., Ko, D., Jeon, M.: Key points estimation and point instance segmentation approach for lane detection. ArXiv arXiv:abs\/2002.06604 (2020)"},{"key":"21_CR6","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1109\/TITS.2019.2890870","volume":"21","author":"X Li","year":"2020","unstructured":"Li, X., Li, J.Y., Hu, X., Yang, J.: Line-cnn: End-to-end traffic line detection with line proposal unit. IEEE Trans. Intell. Transp. Syst. 21, 248\u2013258 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"21_CR7","unstructured":"Liang, Y., Li, X., Jafari, N.H., Chen, Q.: Video object segmentation with adaptive feature bank and uncertain-region refinement. ArXiv arXiv:abs\/2010.07958 (2020)"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Liu, R., Yuan, Z., Liu, T., Xiong, Z.: End-to-end lane shape prediction with transformers. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 3693\u20133701 (2020)","DOI":"10.1109\/WACV48630.2021.00374"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Neven, D., Brabandere, B.D., Georgoulis, S., Proesmans, M., Gool, L.V.: Towards end-to-end lane detection: an instance segmentation approach. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 286\u2013291 (2018)","DOI":"10.1109\/IVS.2018.8500547"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Pan, X., Shi, J., Luo, P., Wang, X., Tang, X.: Spatial as deep: Spatial cnn for traffic scene understanding. In: AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v32i1.12301"},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Qin, Z., Wang, H., Li, X.: Ultra fast structure-aware deep lane detection. ArXiv arXiv:abs\/2004.11757 (2020)","DOI":"10.1007\/978-3-030-58586-0_17"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Qu, Z., Jin, H., Zhou, Y., Yang, Z., Zhang, W.: Focus on local: Detecting lane marker from bottom up via key point. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14117\u201314125 (2021)","DOI":"10.1109\/CVPR46437.2021.01390"},{"key":"21_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2023.103771","volume":"91","author":"P Shi","year":"2023","unstructured":"Shi, P., Zhang, C., Xu, S., Qi, H., Chen, X.: Mt-net: Fast video instance lane detection based on space time memory and template matching. J. Vis. Commun. Image Represent. 91, 103771 (2023)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Tabelini, L., Berriel, R., Paix\u00e3o, T.M., Badue, C.S., de\u00a0Souza, A.F., Oliveira-Santos, T.: Keep your eyes on the lane: Real-time attention-guided lane detection. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 294\u2013302 (2020)","DOI":"10.1109\/CVPR46437.2021.00036"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Tabelini, L., Berriel, R., Paix\u00e3o, T.M., Badue, C.S., de\u00a0Souza, A.F., Oliveira-Santos, T.: Polylanenet: Lane estimation via deep polynomial regression. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 6150\u20136156 (2020)","DOI":"10.1109\/ICPR48806.2021.9412265"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Tabelini, L., Berriel, R., de\u00a0Souza, A.F., Badue, C.S., Oliveira-Santos, T.: Lane marking detection and classification using spatial-temporal feature pooling. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20137 (2022)","DOI":"10.1109\/IJCNN55064.2022.9892478"},{"key":"21_CR17","unstructured":"Tolstikhin, I.O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., Lucic, M., Dosovitskiy, A.: Mlp-mixer: An all-mlp architecture for vision. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol.\u00a034, pp. 24261\u201324272. Curran Associates, Inc. (2021)"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Ventura, C., Bellver, M., Girbau, A., Salvador, A., Marqu\u00e9s, F., i\u00a0Nieto, X.G.: Rvos: End-to-end recurrent network for video object segmentation. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5272\u20135281 (2019)","DOI":"10.1109\/CVPR.2019.00542"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Xie, H., Yao, H., Zhou, S., Zhang, S., Sun, W.: Efficient regional memory network for video object segmentation. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1286\u20131295 (2021)","DOI":"10.1109\/CVPR46437.2021.00134"},{"key":"21_CR20","volume-title":"Polylanenet++: enhancing the polynomial regression lane detection based on spatio-temporal fusion","author":"C Yang","year":"2024","unstructured":"Yang, C., Tian, Z., You, X., Jia, K., Liu, T., Pan, Z., John, V.: Polylanenet++: enhancing the polynomial regression lane detection based on spatio-temporal fusion. Signal, Image and Video Processing (2024)"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, J., Deng, T., Yan, F., Liu, W.: Lane detection model based on spatio-temporal network with double convolutional gated recurrent units. IEEE Trans. Intell. Transp. Syst. 23, 6666\u20136678 (2021). https:\/\/api.semanticscholar.org\/CorpusID:234354290","DOI":"10.1109\/TITS.2021.3060258"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wu, Z., Peng, H., Lin, S.: A transductive approach for video object segmentation. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6947\u20136956 (2020)","DOI":"10.1109\/CVPR42600.2020.00698"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhu, L., Feng, W., Fu, H., Wang, M., Li, Q., Li, C., Wang, S.: Vil-100: A new dataset and a baseline model for video instance lane detection. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 15661\u201315670 (2021)","DOI":"10.1109\/ICCV48922.2021.01539"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Zheng, T., Fang, H., Zhang, Y., Tang, W., Yang, Z., Liu, H., Cai, D.: Resa: Recurrent feature-shift aggregator for lane detection. In: AAAI Conference on Artificial Intelligence (2020)","DOI":"10.1609\/aaai.v35i4.16469"},{"key":"21_CR25","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TVT.2019.2949603","volume":"69","author":"Q Zou","year":"2019","unstructured":"Zou, Q., Jiang, H., Dai, Q., Yue, Y., Chen, L., Wang, Q.: Robust lane detection from continuous driving scenes using deep neural networks. IEEE Trans. Veh. Technol. 69, 41\u201354 (2019)","journal-title":"IEEE Trans. Veh. Technol."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8490-5_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T09:14:58Z","timestamp":1730884498000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8490-5_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,7]]},"ISBN":["9789819784899","9789819784905"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8490-5_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,7]]},"assertion":[{"value":"7 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}