{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T03:25:15Z","timestamp":1781234715088,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072334,61902275,U1803264"],"award-info":[{"award-number":["62072334,61902275,U1803264"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3503161.3547914","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T15:43:01Z","timestamp":1665416581000},"page":"2324-2332","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Video Instance Lane Detection via Deep Temporal and Geometry Consistency Constraints"],"prefix":"10.1145","author":[{"given":"Mingqian","family":"Wang","sequence":"first","affiliation":[{"name":"Tianjin University, Tian Jin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tianjin University, Tian Jin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Feng","sequence":"additional","affiliation":[{"name":"Tianjin University, Tian Jin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology (Guangzhou) &amp; The Hong Kong University of Science and Technology, Guang zhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Wang","sequence":"additional","affiliation":[{"name":"University of South Carolina, Columbia, SC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"e_1_3_2_2_1_1","first-page":"8","article-title":"Video Segmentation by Non-Local Consensus Voting","volume":"2","author":"Faktor Alon","year":"2014","unstructured":"Alon Faktor and Michal Irani . 2014 . Video Segmentation by Non-Local Consensus Voting . In BMVC , Vol. 2. 8 . Alon Faktor and Michal Irani. 2014. Video Segmentation by Non-Local Consensus Voting. In BMVC, Vol. 2. 8.","journal-title":"BMVC"},{"key":"e_1_3_2_2_2_1","article-title":"Research on the Lane Detection Algorithm Based on Zoning Hough Transformation. In Advanced Materials Research, Vol. 490","author":"Fan Chao","year":"2012","unstructured":"Chao Fan , Li Long Hou , Shuai Di , and Jing Bo Xu . 2012 . Research on the Lane Detection Algorithm Based on Zoning Hough Transformation. In Advanced Materials Research, Vol. 490 . Trans Tech Publ, 1862--1866. Chao Fan, Li Long Hou, Shuai Di, and Jing Bo Xu. 2012. Research on the Lane Detection Algorithm Based on Zoning Hough Transformation. In Advanced Materials Research, Vol. 490. Trans Tech Publ, 1862--1866.","journal-title":"Trans Tech Publ, 1862--1866."},{"key":"e_1_3_2_2_3_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778.  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Yuenan Hou Zheng Ma Chunxiao Liu Tak-Wai Hui and Chen Change Loy. 2020. Inter-region affinity distillation for road marking segmentation. In CVPR. 12486--12495.  Yuenan Hou Zheng Ma Chunxiao Liu Tak-Wai Hui and Chen Change Loy. 2020. Inter-region affinity distillation for road marking segmentation. In CVPR. 12486--12495.","DOI":"10.1109\/CVPR42600.2020.01250"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Yuenan Hou Zheng Ma Chunxiao Liu and Chen Change Loy. 2019. Learning lightweight lane detection cnns by self attention distillation. In ICCV. 1013--1021.  Yuenan Hou Zheng Ma Chunxiao Liu and Chen Change Loy. 2019. Learning lightweight lane detection cnns by self attention distillation. In ICCV. 1013--1021.","DOI":"10.1109\/ICCV.2019.00110"},{"key":"e_1_3_2_2_6_1","unstructured":"Yuanting Hu Jiabin Huang and Alexander G Schwing. 2018. Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation. In ECCV. 786--802.  Yuanting Hu Jiabin Huang and Alexander G Schwing. 2018. Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation. In ECCV. 786--802."},{"key":"e_1_3_2_2_7_1","volume-title":"FusionSeg: Learning to combine motion and appearance for fully automatic segmentation of generic objects in videos","author":"Jain Suyog Dutt","unstructured":"Suyog Dutt Jain , Bo Xiong , and Kristen Grauman . 2017. FusionSeg: Learning to combine motion and appearance for fully automatic segmentation of generic objects in videos . In CVPR. IEEE , 2117--2126. Suyog Dutt Jain, Bo Xiong, and Kristen Grauman. 2017. FusionSeg: Learning to combine motion and appearance for fully automatic segmentation of generic objects in videos. In CVPR. IEEE, 2117--2126."},{"key":"e_1_3_2_2_8_1","volume-title":"Fahad Shahbaz Khan, and Michael Felsberg","author":"Johnander Joakim","year":"2019","unstructured":"Joakim Johnander , Martin Danelljan , Emil Brissman , Fahad Shahbaz Khan, and Michael Felsberg . 2019 . A Generative Appearance Model for End-to-end Video Object Segmentation. In CVPR. 8953--8962. Joakim Johnander, Martin Danelljan, Emil Brissman, Fahad Shahbaz Khan, and Michael Felsberg. 2019. A Generative Appearance Model for End-to-end Video Object Segmentation. In CVPR. 8953--8962."},{"key":"e_1_3_2_2_9_1","volume-title":"Seunghak Shin, Oleksandr Bailo, Namil Kim, Tae-Hee Lee, Hyun Seok Hong, Seung-Hoon Han, and So Kweon.","author":"Lee Seokju","year":"2017","unstructured":"Seokju Lee , Junsik Kim , Jae Shin Yoon , Seunghak Shin, Oleksandr Bailo, Namil Kim, Tae-Hee Lee, Hyun Seok Hong, Seung-Hoon Han, and So Kweon. 2017 . Vpgnet : Vanishing point guided network for lane and road marking detection and recognition. In ICCV. 1947--1955. Seokju Lee, Junsik Kim, Jae Shin Yoon, Seunghak Shin, Oleksandr Bailo, Namil Kim, Tae-Hee Lee, Hyun Seok Hong, Seung-Hoon Han, and So Kweon. 2017. Vpgnet: Vanishing point guided network for lane and road marking detection and recognition. In ICCV. 1947--1955."},{"key":"e_1_3_2_2_10_1","volume-title":"Key-Segments for Video Object Segmentation","author":"Lee Yong Jae","year":"1995","unstructured":"Yong Jae Lee , Jaechul Kim , and Kristen Grauman . 2011. Key-Segments for Video Object Segmentation . In ICCV. IEEE , 1995 --2002. Yong Jae Lee, Jaechul Kim, and Kristen Grauman. 2011. Key-Segments for Video Object Segmentation. In ICCV. IEEE, 1995--2002."},{"key":"e_1_3_2_2_11_1","volume-title":"Road Lane Detection With Gabor filters","author":"Li Zuo-Quan","unstructured":"Zuo-Quan Li , Hui-Min Ma , and Zheng-Yu Liu . 2016. Road Lane Detection With Gabor filters . In ISAI. IEEE , 436--440. Zuo-Quan Li, Hui-Min Ma, and Zheng-Yu Liu. 2016. Road Lane Detection With Gabor filters. In ISAI. IEEE, 436--440."},{"key":"e_1_3_2_2_12_1","first-page":"3430","article-title":"Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement","volume":"33","author":"Liang Yongqing","year":"2020","unstructured":"Yongqing Liang , Xin Li , Navid Jafari , and Jim Chen . 2020 . Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement . NeurIPS 33 (2020), 3430 -- 3441 . Yongqing Liang, Xin Li, Navid Jafari, and Jim Chen. 2020. Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement. NeurIPS 33 (2020), 3430--3441.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_13_1","unstructured":"Ruijin Liu Zejian Yuan Tie Liu and Zhiliang Xiong. 2021. End-to-end lane shape prediction with transformers. In WACV. 3694--3702.  Ruijin Liu Zejian Yuan Tie Liu and Zhiliang Xiong. 2021. End-to-end lane shape prediction with transformers. In WACV. 3694--3702."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2015.09.009"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2006.869595"},{"key":"e_1_3_2_2_16_1","volume-title":"Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool.","author":"Neven Davy","year":"2018","unstructured":"Davy Neven , Bert De Brabandere , Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool. 2018 . Towards End-to-End Lane Detection: an Instance Segmentation Approach. In IV. IEEE , 286--291. Davy Neven, Bert De Brabandere, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool. 2018. Towards End-to-End Lane Detection: an Instance Segmentation Approach. In IV. IEEE, 286--291."},{"key":"e_1_3_2_2_17_1","volume-title":"Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions","author":"Ochs Peter","unstructured":"Peter Ochs and Thomas Brox . 2011. Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions . In ICCV. IEEE , 1583--1590. Peter Ochs and Thomas Brox. 2011. Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions. In ICCV. IEEE, 1583--1590."},{"key":"e_1_3_2_2_18_1","unstructured":"Seoung Wug Oh Joon-Young Lee Ning Xu and Seon Joo Kim. 2019. Video Object Segmentation using Space-Time Memory Networks. In ICCV. 9226--9235.  Seoung Wug Oh Joon-Young Lee Ning Xu and Seon Joo Kim. 2019. Video Object Segmentation using Space-Time Memory Networks. In ICCV. 9226--9235."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12301"},{"key":"e_1_3_2_2_20_1","volume-title":"Markus Gross, and Alexander Sorkine-Hornung.","author":"Perazzi Federico","year":"2016","unstructured":"Federico Perazzi , Jordi Pont-Tuset , Brian McWilliams , Luc Van Gool , Markus Gross, and Alexander Sorkine-Hornung. 2016 . A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation. In CVPR. 724--732. Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc Van Gool, Markus Gross, and Alexander Sorkine-Hornung. 2016. A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation. In CVPR. 724--732."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Zequn Qin Huanyu Wang and Xi Li. 2020. Ultra fast structure-aware deep lane detection. In ECCV. 276--291.  Zequn Qin Huanyu Wang and Xi Li. 2020. Ultra fast structure-aware deep lane detection. In ECCV. 276--291.","DOI":"10.1007\/978-3-030-58586-0_17"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Hongje Seong Junhyuk Hyun and Euntai Kim. 2020. Kernelized Memory Network for Video Object Segmentation. In ECCV. 629--645.  Hongje Seong Junhyuk Hyun and Euntai Kim. 2020. Kernelized Memory Network for Video Object Segmentation. In ECCV. 629--645.","DOI":"10.1007\/978-3-030-58542-6_38"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Hongmei Song Wenguan Wang Sanyuan Zhao Jianbing Shen and Kin-Man Lam. 2018. Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection. In ECCV. 715--731.  Hongmei Song Wenguan Wang Sanyuan Zhao Jianbing Shen and Kin-Man Lam. 2018. Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection. In ECCV. 715--731.","DOI":"10.1007\/978-3-030-01252-6_44"},{"key":"e_1_3_2_2_24_1","volume-title":"Structure Guided Lane Detection. IJCAI","author":"Su Jinming","year":"2021","unstructured":"Jinming Su , Chao Chen , Ke Zhang , Junfeng Luo , Xiaoming Wei , and Xiaolin Wei . 2021. Structure Guided Lane Detection. IJCAI ( 2021 ). Jinming Su, Chao Chen, Ke Zhang, Junfeng Luo, Xiaoming Wei, and Xiaolin Wei. 2021. Structure Guided Lane Detection. IJCAI (2021)."},{"key":"e_1_3_2_2_25_1","volume-title":"HSI Color Model Based Lane-Marking Detection","author":"Sun Tsung-Ying","unstructured":"Tsung-Ying Sun , Shang-Jeng Tsai , and Vincent Chan . 2006. HSI Color Model Based Lane-Marking Detection . In ITSC. IEEE , 1168--1172. Tsung-Ying Sun, Shang-Jeng Tsai, and Vincent Chan. 2006. HSI Color Model Based Lane-Marking Detection. In ITSC. IEEE, 1168--1172."},{"key":"e_1_3_2_2_26_1","volume-title":"Alberto F De Souza, and Thiago Oliveira-Santos","author":"Tabelini Lucas","year":"2021","unstructured":"Lucas Tabelini , Rodrigo Berriel , Thiago M Paixao , Claudine Badue , Alberto F De Souza, and Thiago Oliveira-Santos . 2021 . Keep your eyes on the lane: Real-time attention-guided lane detection. In CVPR. 294--302. Lucas Tabelini, Rodrigo Berriel, Thiago M Paixao, Claudine Badue, Alberto F De Souza, and Thiago Oliveira-Santos. 2021. Keep your eyes on the lane: Real-time attention-guided lane detection. In CVPR. 294--302."},{"key":"e_1_3_2_2_27_1","volume-title":"Alberto F De Souza, and Thiago Oliveira-Santos","author":"Tabelini Lucas","year":"2021","unstructured":"Lucas Tabelini , Rodrigo Berriel , Thiago M Paixao , Claudine Badue , Alberto F De Souza, and Thiago Oliveira-Santos . 2021 . Polylanenet : Lane estimation via deep polynomial regression. In ICPR. IEEE , 6150--6156. Lucas Tabelini, Rodrigo Berriel, Thiago M Paixao, Claudine Badue, Alberto F De Souza, and Thiago Oliveira-Santos. 2021. Polylanenet: Lane estimation via deep polynomial regression. In ICPR. IEEE, 6150--6156."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107623"},{"key":"e_1_3_2_2_29_1","volume-title":"Jyh-Jing Hwang.","author":"Ziwei Liu","year":"2018","unstructured":"Ziwei Liu Stella X. Yu Tsung-Wei Ke , Jyh-Jing Hwang. 2018 . Adaptive Affinity Fields for Semantic Segmentation. In ECCV. Springer , 605--621. Ziwei Liu Stella X. Yu Tsung-Wei Ke, Jyh-Jing Hwang. 2018. Adaptive Affinity Fields for Semantic Segmentation. In ECCV. Springer, 605--621."},{"key":"e_1_3_2_2_30_1","unstructured":"TuSimple. 2017. http:\/\/benchmark.tusimple.ai.  TuSimple. 2017. http:\/\/benchmark.tusimple.ai."},{"key":"e_1_3_2_2_31_1","volume-title":"NeurIPS 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , Lukasz Kaiser , and Illia Polosukhin . 2017. Attention Is All You Need. NeurIPS 30 ( 2017 ). Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. NeurIPS 30 (2017)."},{"key":"e_1_3_2_2_32_1","volume-title":"RVOS: End-to-End Recurrent Network for Video Object Segmentation. In CVPR. 5277--5286.","author":"Ventura Carles","year":"2019","unstructured":"Carles Ventura , Miriam Bellver , Andreu Girbau , Amaia Salvador , Ferran Marques , and Xavier Giro-i Nieto . 2019 . RVOS: End-to-End Recurrent Network for Video Object Segmentation. In CVPR. 5277--5286. Carles Ventura, Miriam Bellver, Andreu Girbau, Amaia Salvador, Ferran Marques, and Xavier Giro-i Nieto. 2019. RVOS: End-to-End Recurrent Network for Video Object Segmentation. In CVPR. 5277--5286."},{"key":"e_1_3_2_2_33_1","volume-title":"An Approach of Lane Detection Based on Inverse Perspective Mapping","author":"Wang Jun","unstructured":"Jun Wang , Tao Mei , Bin Kong , and Hu Wei . 2014. An Approach of Lane Detection Based on Inverse Perspective Mapping . In ITSC. IEEE , 35--38. Jun Wang, Tao Mei, Bin Kong, and Hu Wei. 2014. An Approach of Lane Detection Based on Inverse Perspective Mapping. In ITSC. IEEE, 35--38."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Yuqing Wang Zhaoliang Xu Xinlong Wang Chunhua Shen Baoshan Cheng Hao Shen and Huaxia Xia. 2021. End-to-End Video Instance Segmentation with Transformers. In CVPR. 8741--9750.  Yuqing Wang Zhaoliang Xu Xinlong Wang Chunhua Shen Baoshan Cheng Hao Shen and Huaxia Xia. 2021. End-to-End Video Instance Segmentation with Transformers. In CVPR. 8741--9750.","DOI":"10.1109\/CVPR46437.2021.00863"},{"key":"e_1_3_2_2_35_1","unstructured":"Haozhe Xie Hongxun Yao Shangchen Zhou Shengping Zhang and Wenxiu Sun. 2021. Efficient Regional Memory Network for Video Object Segmentation. In CVPR. 1286--1295.  Haozhe Xie Hongxun Yao Shangchen Zhou Shengping Zhang and Wenxiu Sun. 2021. Efficient Regional Memory Network for Video Object Segmentation. In CVPR. 1286--1295."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"crossref","unstructured":"Yizhuo Zhang Zhirong Wu Houwen Peng and Stephen Lin. 2020. A Transductive Approach for Video Object Segmentation. In CVPR. 6949--6958.  Yizhuo Zhang Zhirong Wu Houwen Peng and Stephen Lin. 2020. A Transductive Approach for Video Object Segmentation. In CVPR. 6949--6958.","DOI":"10.1109\/CVPR42600.2020.00698"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"crossref","unstructured":"Yujun Zhang Lei Zhu Wei Feng Huazhu Fu Mingqian Wang Qingxia Li Cheng Li and Song Wang. 2021. VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection. In ICCV. 15681--15690.  Yujun Zhang Lei Zhu Wei Feng Huazhu Fu Mingqian Wang Qingxia Li Cheng Li and Song Wang. 2021. VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection. In ICCV. 15681--15690.","DOI":"10.1109\/ICCV48922.2021.01539"},{"key":"e_1_3_2_2_38_1","volume-title":"Resa: Recurrent feature-shift aggregator for lane detection. AAAI 5, 7","author":"Zheng Tu","year":"2020","unstructured":"Tu Zheng , Hao Fang , Yi Zhang , Wenjian Tang , Zheng Yang , Haifeng Liu , and Deng Cai . 2020 . Resa: Recurrent feature-shift aggregator for lane detection. AAAI 5, 7 (2020). Tu Zheng, Hao Fang, Yi Zhang, Wenjian Tang, Zheng Yang, Haifeng Liu, and Deng Cai. 2020. Resa: Recurrent feature-shift aggregator for lane detection. AAAI 5, 7 (2020)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7008"}],"event":{"name":"MM '22: The 30th ACM International Conference on Multimedia","location":"Lisboa Portugal","acronym":"MM '22","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 30th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3547914","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3547914","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:30Z","timestamp":1750186830000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3547914"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":39,"alternative-id":["10.1145\/3503161.3547914","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3547914","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2022-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}