{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:11:12Z","timestamp":1743041472374,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030406042"},{"type":"electronic","value":"9783030406059"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-40605-9_45","type":"book-chapter","created":{"date-parts":[[2020,2,5]],"date-time":"2020-02-05T14:03:51Z","timestamp":1580911431000},"page":"529-540","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Vehicles Tracking by Combining Convolutional Neural Network Based Segmentation and Optical Flow Estimation"],"prefix":"10.1007","author":[{"given":"Tuan-Hung","family":"Vu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacques","family":"Boonaert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastien","family":"Ambellouis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdelmalik Taleb","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,2,6]]},"reference":[{"key":"45_CR1","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.B.: Mask R-CNN. In: IEEE International Conference on Computer Vision, Italy (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"45_CR2","doi-asserted-by":"crossref","unstructured":"Bochinski, E., Eiselein, V., Sikora, T.: High-speed tracking-by-detection without using image information. In: International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS, Italy (2017)","DOI":"10.1109\/AVSS.2017.8078516"},{"key":"45_CR3","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","volume":"110","author":"H Bay","year":"2008","unstructured":"Bay, H., Ess, A., Tuytelaars, T.V., Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110, 346\u2013359 (2008)","journal-title":"Comput. Vis. Image Underst."},{"key":"45_CR4","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1109\/TPAMI.2010.143","volume":"33","author":"T Brox","year":"2011","unstructured":"Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 500\u2013513 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"45_CR5","doi-asserted-by":"crossref","unstructured":"Lyu, S., et al.: UA-DETRAC 2017: report of AVSS2017 & IWT4S challenge on advanced traffic monitoring. In: 14th IEEE International Conference on Advanced Video and Signal Based Surveillance AVSS (2017)","DOI":"10.1109\/AVSS.2017.8078560"},{"key":"45_CR6","doi-asserted-by":"crossref","unstructured":"Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. TPAMI (2017)","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"45_CR7","doi-asserted-by":"crossref","unstructured":"Wang, L., Lu, Y., Wang, H., Zheng, Y., Ye, H., Xue, X.: Evolving boxes for fast vehicle detection. In: IEEE International Conference on Multimedia and Expo ICME (2017)","DOI":"10.1109\/ICME.2017.8019461"},{"key":"45_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"45_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/978-3-030-01240-3_36","volume-title":"Computer Vision \u2013 ECCV 2018","author":"P Luc","year":"2018","unstructured":"Luc, P., Couprie, C., LeCun, Y., Verbeek, J.: Predicting future instance segmentation by forecasting convolutional features. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 593\u2013608. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01240-3_36"},{"key":"45_CR10","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross. M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: Computer Vision and Pattern Recognition CVPR (2016)","DOI":"10.1109\/CVPR.2016.85"},{"key":"45_CR11","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: The IEEE Conference on Computer Vision and Pattern Recognition CVPR (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"45_CR12","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/s11263-012-0594-8","volume":"103","author":"H Wang","year":"2013","unstructured":"Wang, H., Klaser, A., Schmid, C., Liu, C.-L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. IJCV 103, 60\u201379 (2013)","journal-title":"Int. J. Comput. Vis. IJCV"},{"key":"45_CR13","doi-asserted-by":"crossref","unstructured":"Wang, H., Schmid, C.: Action recognition with improved trajectories. In: IEEE International Conference on Computer Vision ICCV (2013)","DOI":"10.1109\/ICCV.2013.441"},{"key":"45_CR14","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems NIPS (2014)"},{"key":"45_CR15","doi-asserted-by":"crossref","unstructured":"Wang, L., Qiao, Y., Tang, X.: Action recognition with trajectory-pooled deep-convolutional descriptors. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR (2015)","DOI":"10.1109\/CVPR.2015.7299059"},{"key":"45_CR16","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. TPAMI 39, 640\u2013651 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. TPAMI"},{"key":"45_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"45_CR18","unstructured":"Roth, S.: Discrete-continuous optimization for multi-target tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR (2012)"},{"key":"45_CR19","doi-asserted-by":"crossref","unstructured":"Bae, S., Yoon, K.: Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR (2014)","DOI":"10.1109\/CVPR.2014.159"},{"key":"45_CR20","doi-asserted-by":"crossref","unstructured":"Dicle, C., Camps, O.I., Sznaier, M.: The way they move: tracking multiple targets with similar appearance. In: IEEE International Conference on Computer Vision ICCV (2013)","DOI":"10.1109\/ICCV.2013.286"},{"key":"45_CR21","doi-asserted-by":"crossref","unstructured":"Wen, L., Li, W., Yan, J., Lei, Z., Yi, D., Li, S.Z.: Multiple target tracking based on undirected hierarchical relation hypergraph. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR (2014)","DOI":"10.1109\/CVPR.2014.167"},{"key":"45_CR22","doi-asserted-by":"crossref","unstructured":"Ilg, E., Mayer, N., Saikia, T., Keuper, K., Dosovitskiy, A., Brox, T.: FlowNet 2.0: evolution of optical flow estimation with deep networks. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR (2017)","DOI":"10.1109\/CVPR.2017.179"},{"key":"45_CR23","doi-asserted-by":"crossref","unstructured":"Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR (2018)","DOI":"10.1109\/CVPR.2018.00931"},{"key":"45_CR24","unstructured":"Villegas, R., Yang, J., Zou, Y., Sohn, S., Lin, X., Lee, H.: Learning to generate long-term future via hierarchical prediction. In: Proceedings of the 34th International Conference on Machine Learning ICML (2017)"},{"key":"45_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1007\/978-3-319-46478-7_51","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Walker","year":"2016","unstructured":"Walker, J., Doersch, C., Gupta, A., Hebert, M.: An uncertain future: forecasting from static images using variational autoencoders. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 835\u2013851. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_51"},{"key":"45_CR26","unstructured":"Mathieu, M., Couprie, C., LeCun, Y.: Deep multi-scale video prediction beyond mean square error. In: International Conference on Learning Representations ICLR (2016)"},{"key":"45_CR27","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: International Conference on Learning Representations ICLR (2016)"},{"key":"45_CR28","unstructured":"Vondrick, C., Pirsiavash, H., Torralba, A.: Anticipating the future by watching unlabeled video. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR (2016)"},{"key":"45_CR29","doi-asserted-by":"crossref","unstructured":"Chen, Q., Koltun, V.: Full flow: optical flow estimation by global optimization over regular grids. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR (2016)","DOI":"10.1109\/CVPR.2016.509"}],"container-title":["Lecture Notes in Computer Science","Advanced Concepts for Intelligent Vision Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-40605-9_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T15:08:55Z","timestamp":1709824135000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-40605-9_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030406042","9783030406059"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-40605-9_45","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"6 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIVS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Concepts for Intelligent Vision Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 February 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 February 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acivs2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/acivs.org\/acivs2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}