{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T15:27:45Z","timestamp":1726068465806},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030412982"},{"type":"electronic","value":"9783030412999"}],"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\/978-3-030-41299-9_27","type":"book-chapter","created":{"date-parts":[[2020,2,22]],"date-time":"2020-02-22T09:02:51Z","timestamp":1582362171000},"page":"351-361","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Background Subtraction Based on Encoder-Decoder Structured CNN"],"prefix":"10.1007","author":[{"given":"Jingming","family":"Wang","sequence":"first","affiliation":[]},{"given":"Kwok Leung","family":"Chan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,2,23]]},"reference":[{"key":"27_CR1","doi-asserted-by":"crossref","unstructured":"Bouwmans, T.: Background subtraction for visual surveillance: a fuzzy approach. In: Handbook on Soft Computing for Video Surveillance. Taylor and Francis Group (2012)","DOI":"10.1201\/b11631-6"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"El Baf, F., Bouwmans, T.: Comparison of background subtraction methods for a multimedia learning space. In: Proceedings of International Conference on Signal Processing and Multimedia (2007)","DOI":"10.1109\/IWSSIP.2007.4381122"},{"issue":"3","key":"27_CR3","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1109\/TMM.2008.917403","volume":"10","author":"J-W Hsieh","year":"2008","unstructured":"Hsieh, J.-W., Hsu, Y.-T., Liao, H.-Y.M., Chen, C.-C.: Video-based human movement analysis and its application to surveillance systems. IEEE Trans. Multimedia 10(3), 372\u2013384 (2008)","journal-title":"IEEE Trans. Multimedia"},{"issue":"2","key":"27_CR4","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1109\/TCSVT.2003.821980","volume":"14","author":"W Lu","year":"2004","unstructured":"Lu, W., Tan, Y.-P.: A vision-based approach to early detection of drowning incidents in swimming pools. IEEE Trans. Circuits Syst. Video Technol. 14(2), 159\u2013178 (2004)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"27_CR5","first-page":"262","volume-title":"Lecture Notes in Computer Science","author":"Rene Visser","year":"2002","unstructured":"Visser, R., Sebe, N., Bakker, E.: Object recognition for video retrieval. In: Proceedings of International Conference on Image and Video Retrieval, pp. 250\u2013259 (2002)"},{"key":"27_CR6","doi-asserted-by":"publisher","first-page":"32","DOI":"10.2174\/1874479610801010032","volume":"1","author":"SY Elhabian","year":"2008","unstructured":"Elhabian, S.Y., El-Sayed, K.M., Ahmed, S.H.: Moving object detection in spatial domain using background removal techniques \u2013 state-of-art. Recent Pat. Comput. Sci. 1, 32\u201354 (2008)","journal-title":"Recent Pat. Comput. Sci."},{"issue":"3","key":"27_CR7","first-page":"147","volume":"4","author":"T Bouwmans","year":"2011","unstructured":"Bouwmans, T.: Recent advanced statistical background modeling for foreground detection - a systematic survey. Recent Pat. Comput. Sci. 4(3), 147\u2013176 (2011)","journal-title":"Recent Pat. Comput. Sci."},{"issue":"8","key":"27_CR8","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1109\/34.868677","volume":"22","author":"C Stauffer","year":"2000","unstructured":"Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747\u2013757 (2000)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"27_CR9","doi-asserted-by":"crossref","unstructured":"Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of International Conference on Pattern Recognition, pp. 28\u201331 (2004)","DOI":"10.1109\/ICPR.2004.1333992"},{"issue":"7","key":"27_CR10","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1109\/JPROC.2002.801448","volume":"90","author":"A Elgammal","year":"2002","unstructured":"Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. IEEE 90(7), 1151\u20131163 (2002)","journal-title":"Proc. IEEE"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Barnich, O., Van Droogenbroeck, M.: ViBE: a powerful random technique to estimate the background in video sequences. In: Proceedings of International Conference Acoustics, Speech and Signal Processing, pp. 945\u2013948 (2009)","DOI":"10.1109\/ICASSP.2009.4959741"},{"issue":"1","key":"27_CR12","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TPAMI.2009.112","volume":"32","author":"V Mahadevan","year":"2010","unstructured":"Mahadevan, V., Vasconcelos, N.: Spatiotemporal saliency in dynamic scenes. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 171\u2013177 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"27_CR13","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1109\/TPAMI.2006.68","volume":"28","author":"M Heikkil\u00e4","year":"2006","unstructured":"Heikkil\u00e4, M., Pietik\u00e4inen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657\u2013662 (2006)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Liao, S., Zhao, G., Kellokumpu, V., Pietik\u00e4inen, M., Li, S.Z.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1301\u20131306 (2010)","DOI":"10.1109\/CVPR.2010.5539817"},{"issue":"1","key":"27_CR15","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1109\/TIP.2014.2378053","volume":"24","author":"P-L St-Charles","year":"2015","unstructured":"St-Charles, P.-L., Bilodeau, G.-A., Bergevin, R.: SuBSENSE: a universal change detection method with local adaptive sensitivity. IEEE Trans. Image Process. 24(1), 359\u2013373 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"27_CR16","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.patrec.2016.09.014","volume":"96","author":"Y Wang","year":"2017","unstructured":"Wang, Y., Luo, Z., Jodoin, P.-M.: Interactive deep learning method for segmenting moving objects. Pattern Recogn. Lett. 96, 66\u201375 (2017)","journal-title":"Pattern Recogn. Lett."},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Lim, L.A., Keles, H.Y.: Foreground segmentation using a triplet convolutional neural network for multiscale feature encoding. arXiv:1801.02225 [cs.CV] (2018)","DOI":"10.1016\/j.patrec.2018.08.002"},{"key":"27_CR18","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.patcog.2017.09.040","volume":"76","author":"M Babaee","year":"2018","unstructured":"Babaee, M., Dinh, D.T., Rigoll, G.: A deep convolutional neural network for video sequence background subtraction. Pattern Recogn. 76, 635\u2013649 (2018)","journal-title":"Pattern Recogn."},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Van Droogenbroeck, M., Paquot, O.: Background subtraction: experiments and improvements for ViBE. In: Proceedings of IEEE Workshop on Change Detection at IEEE Conference on Computer Vision and Pattern Recognition, pp. 32\u201337 (2012)","DOI":"10.1109\/CVPRW.2012.6238924"},{"key":"27_CR20","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1007\/s00138-012-0448-y","volume":"24","author":"SW Kim","year":"2013","unstructured":"Kim, S.W., Yun, K., Yi, K.M., Kim, S.J., Choi, J.Y.: Detection of moving objects with a moving camera using non-panoramic background model. Mach. Vis. Appl. 24, 1015\u20131028 (2013)","journal-title":"Mach. Vis. Appl."},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Lauguard, B., Pi\u00e9rard, S., Van Droogenbroeck, M.: LaBGen-P: a pixel-level stationary background generation method based on LaBGen. In: Proceedings of IEEE International Conference on Pattern Recognition, pp. 107\u2013113 (2016)","DOI":"10.1109\/ICPR.2016.7899617"},{"key":"27_CR22","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":"27_CR23","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jodoin, P.-M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P.: CDnet 2014: an expanded change detection benchmark dataset. In: Proceedings of IEEE Workshop on Change Detection at CVPR-2014, pp. 387\u2013394 (2014)","DOI":"10.1109\/CVPRW.2014.126"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of International Conference on Computer Vision (2015)","DOI":"10.1109\/ICCV.2015.178"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"St-Charles, P.-L., Bilodeau, G.-A., Bergevin, R.: A self-adjusting approach to change detection based on background word consensus. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision, pp. 990\u2013997 (2015)","DOI":"10.1109\/WACV.2015.137"},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Wang, R., Bunyak, F., Seetharaman, G., Palaniappan, K.: Static and moving object detection using flux tensor with split gaussian models. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (2014)","DOI":"10.1109\/CVPRW.2014.68"},{"key":"27_CR27","unstructured":"Chen, Y., Wang, J., Lu, H.: Learning sharable models for robust background subtraction. In: Proceedings of IEEE International Conference on Multimedia and Expo (2015)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-41299-9_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T08:07:46Z","timestamp":1665907666000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-41299-9_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030412982","9783030412999"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-41299-9_27","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":"23 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2019a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.acpr2019.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"214","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"125","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"for ACPR 2019 Workshops volume accepted 17 full papers and 6 short papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}