{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:04:21Z","timestamp":1743026661356,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811540141"},{"type":"electronic","value":"9789811540158"}],"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\/978-981-15-4015-8_28","type":"book-chapter","created":{"date-parts":[[2020,3,28]],"date-time":"2020-03-28T10:02:46Z","timestamp":1585389766000},"page":"311-322","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Novel Saliency-Based Cascaded Approach for Moving Object Segmentation"],"prefix":"10.1007","author":[{"given":"Prashant W.","family":"Patil","sequence":"first","affiliation":[]},{"given":"Akshay","family":"Dudhane","sequence":"additional","affiliation":[]},{"given":"Subrahmanyam","family":"Murala","sequence":"additional","affiliation":[]},{"given":"Anil B.","family":"Gonde","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,3,29]]},"reference":[{"key":"28_CR1","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."},{"issue":"6","key":"28_CR2","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1109\/TEVC.2017.2694160","volume":"21","author":"S Bianco","year":"2017","unstructured":"Bianco, S., Ciocca, G., Schettini, R.: Combination of video change detection algorithms by genetic programming. IEEE Trans. Evol. Comput. 21(6), 914\u2013928 (2017)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"28_CR3","unstructured":"Biradar, K.M., Gupta, A., Mandal, M., Vipparthi, S.K.: Challenges in time-stamp aware anomaly detection in traffic videos. arXiv preprint \narXiv:1906.04574\n\n (2019)"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Braham, M., Pi\u00e9rard, S., Van Droogenbroeck, M.: Semantic background subtraction. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 4552\u20134556. IEEE (2017)","DOI":"10.1109\/ICIP.2017.8297144"},{"issue":"11","key":"28_CR5","doi-asserted-by":"publisher","first-page":"5187","DOI":"10.1109\/TIP.2016.2598681","volume":"25","author":"B Cai","year":"2016","unstructured":"Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: DehazeNet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187\u20135198 (2016)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"28_CR6","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1049\/iet-cvi.2018.5020","volume":"13","author":"S Chaudhary","year":"2018","unstructured":"Chaudhary, S., Murala, S.: Depth-based end-to-end deep network for human action recognition. IET Comput. Vision 13(1), 15\u201322 (2018)","journal-title":"IET Comput. Vision"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Chaudhary, S., Murala, S.: TSNet: deep network for human action recognition in hazy videos. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3981\u20133986. IEEE (2018)","DOI":"10.1109\/SMC.2018.00675"},{"key":"28_CR8","unstructured":"Chen, X., Shen, Y., Yang, Y.H.: Background estimation using graph cuts and inpainting. In: Proceedings of Graphics Interface 2010, Canadian Information Processing Society, pp. 97\u2013103 (2010)"},{"key":"28_CR9","doi-asserted-by":"publisher","first-page":"2567","DOI":"10.1109\/TCSVT.2017.2770319","volume":"29","author":"Y Chen","year":"2017","unstructured":"Chen, Y., Wang, J., Zhu, B., Tang, M., Lu, H.: Pixel-wise deep sequence learning for moving object detection. IEEE Trans. Circuits Syst. Video Technol. 29, 2567\u20132579 (2017)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Dudhane, A., Murala, S.: C$$^{\\wedge }$$2MSNet: a novel approach for single image haze removal. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1397\u20131404. IEEE (2018)","DOI":"10.1109\/WACV.2018.00157"},{"issue":"2","key":"28_CR11","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s00138-019-01014-y","volume":"30","author":"A Dudhane","year":"2019","unstructured":"Dudhane, A., Murala, S.: Cardinal color fusion network for single image haze removal. Mach. Vis. Appl. 30(2), 231\u2013242 (2019). \nhttps:\/\/doi.org\/10.1007\/s00138-019-01014-y","journal-title":"Mach. Vis. Appl."},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Dudhane, A., Murala, S.: CDNet: single image de-hazing using unpaired adversarial training. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1147\u20131155. IEEE (2019)","DOI":"10.1109\/WACV.2019.00127"},{"key":"28_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/978-3-319-10584-0_35","volume-title":"Computer Vision \u2013 ECCV 2014","author":"X Guo","year":"2014","unstructured":"Guo, X., Wang, X., Yang, L., Cao, X., Ma, Y.: Robust foreground detection using smoothness and arbitrariness constraints. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 535\u2013550. Springer, Cham (2014). \nhttps:\/\/doi.org\/10.1007\/978-3-319-10584-0_35"},{"issue":"2","key":"28_CR14","doi-asserted-by":"publisher","first-page":"023002","DOI":"10.1117\/1.JEI.27.2.023002","volume":"27","author":"\u015e I\u015f\u0131k","year":"2018","unstructured":"I\u015f\u0131k, \u015e., \u00d6zkan, K., G\u00fcnal, S., Gerek, \u00d6.N.: SWCD: a sliding window and self-regulated learning-based background updating method for change detection in videos. J. Electron. Imaging 27(2), 023002 (2018)","journal-title":"J. Electron. Imaging"},{"key":"28_CR15","doi-asserted-by":"publisher","first-page":"2105","DOI":"10.1109\/TCSVT.2017.2711659","volume":"28","author":"S Jiang","year":"2017","unstructured":"Jiang, S., Lu, X.: WeSamBE: a weight-sample-based method for background subtraction. IEEE Trans. Circuits Syst. Video Technol. 28, 2105\u20132115 (2017)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"6","key":"28_CR16","doi-asserted-by":"publisher","first-page":"3453","DOI":"10.1109\/TITS.2015.2459917","volume":"16","author":"CW Liang","year":"2015","unstructured":"Liang, C.W., Juang, C.F.: Moving object classification using a combination of static appearance features and spatial and temporal entropy values of optical flows. IEEE Trans. Intell. Transp. Syst. 16(6), 3453\u20133464 (2015)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"5","key":"28_CR17","doi-asserted-by":"publisher","first-page":"1641","DOI":"10.1109\/TSP.2009.2014810","volume":"57","author":"HH Lin","year":"2009","unstructured":"Lin, H.H., Liu, T.L., Chuang, J.H.: Learning a scene background model via classification. IEEE Trans. Signal Process. 57(5), 1641\u20131654 (2009)","journal-title":"IEEE Trans. Signal Process."},{"issue":"6","key":"28_CR18","doi-asserted-by":"publisher","first-page":"1208","DOI":"10.1109\/TCSVT.2016.2527258","volume":"27","author":"Y Lin","year":"2017","unstructured":"Lin, Y., Tong, Y., Cao, Y., Zhou, Y., Wang, S.: Visual-attention-based background modeling for detecting infrequently moving objects. IEEE Trans. Circuits Syst. Video Technol. 27(6), 1208\u20131221 (2017)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Patil, P., Murala, S.: FgGAN: a cascaded unpaired learning for background estimation and foreground segmentation. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1770\u20131778. IEEE (2019)","DOI":"10.1109\/WACV.2019.00193"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Patil, P., Murala, S., Dhall, A., Chaudhary, S.: MsEDNet: multi-scale deep saliency learning for moving object detection. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1670\u20131675. IEEE (2018)","DOI":"10.1109\/SMC.2018.00289"},{"key":"28_CR21","doi-asserted-by":"publisher","first-page":"4066","DOI":"10.1109\/TITS.2018.2880096","volume":"20","author":"PW Patil","year":"2018","unstructured":"Patil, P.W., Murala, S.: MSFgNET: a novel compact end-to-end deep network for moving object detection. IEEE Trans. Intell. Transp. Syst. 20, 4066\u20134077 (2018)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"7","key":"28_CR22","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1109\/TCSVT.2017.2669362","volume":"28","author":"SM Roy","year":"2018","unstructured":"Roy, S.M., Ghosh, A.: Real-time adaptive histogram min-max bucket (HMMB) model for background subtraction. IEEE Trans. Circuits Syst. Video Technol. 28(7), 1513\u20131525 (2018)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"10","key":"28_CR23","doi-asserted-by":"publisher","first-page":"4810","DOI":"10.1109\/TIP.2018.2845123","volume":"27","author":"G Shi","year":"2018","unstructured":"Shi, G., Huang, T., Dong, W., Wu, J., Xie, X.: Robust foreground estimation via structured gaussian scale mixture modeling. IEEE Trans. Image Process. 27(10), 4810\u20134824 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"28_CR24","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: 2015 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 990\u2013997. IEEE (2015)","DOI":"10.1109\/WACV.2015.137"},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Thengane, V.G., Gawande, M.B., Dudhane, A.A., Gonde, A.B.: Cycle face aging generative adversarial networks. In: 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS), pp. 125\u2013129. IEEE (2018)","DOI":"10.1109\/ICIINFS.2018.8721435"},{"key":"28_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/978-3-642-33786-4_10","volume-title":"Computer Vision \u2013 ECCV 2012","author":"N Wang","year":"2012","unstructured":"Wang, N., Yao, T., Wang, J., Yeung, D.-Y.: A probabilistic approach to robust matrix factorization. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7578, pp. 126\u2013139. Springer, Heidelberg (2012). \nhttps:\/\/doi.org\/10.1007\/978-3-642-33786-4_10"},{"issue":"1","key":"28_CR27","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/TIP.2017.2754941","volume":"27","author":"W Wang","year":"2018","unstructured":"Wang, W., Shen, J., Shao, L.: Video salient object detection via fully convolutional networks. IEEE Trans. Image Process. 27(1), 38\u201349 (2018)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"28_CR28","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/TPAMI.2017.2662005","volume":"40","author":"W Wang","year":"2018","unstructured":"Wang, W., Shen, J., Yang, R., Porikli, F.: Saliency-aware video object segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 40(1), 20\u201333 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28_CR29","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 the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 387\u2013394 (2014)","DOI":"10.1109\/CVPRW.2014.126"},{"key":"28_CR30","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."},{"issue":"7","key":"28_CR31","doi-asserted-by":"publisher","first-page":"3425","DOI":"10.1109\/TIP.2016.2631900","volume":"26","author":"T Xi","year":"2017","unstructured":"Xi, T., Zhao, W., Wang, H., Lin, W.: Salient object detection with spatiotemporal background priors for video. IEEE Trans. Image Process. 26(7), 3425\u20133436 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"28_CR32","doi-asserted-by":"publisher","first-page":"4945","DOI":"10.1109\/TIE.2017.2669881","volume":"64","author":"CH Yeh","year":"2017","unstructured":"Yeh, C.H., Lin, C.Y., Muchtar, K., Lai, H.E., Sun, M.T.: Three-pronged compensation and hysteresis thresholding for moving object detection in real-time video surveillance. IEEE Trans. Industr. Electron. 64(6), 4945\u20134955 (2017)","journal-title":"IEEE Trans. Industr. Electron."},{"issue":"7","key":"28_CR33","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1109\/TPAMI.2017.2732350","volume":"40","author":"H Yong","year":"2018","unstructured":"Yong, H., Meng, D., Zuo, W., Zhang, L.: Robust online matrix factorization for dynamic background subtraction. IEEE Trans. Pattern Anal. Mach. Intell. 40(7), 1726\u20131740 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28_CR34","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1177\/0361198106194400111","volume":"1944","author":"J Zheng","year":"2006","unstructured":"Zheng, J., Wang, Y., Nihan, N., Hallenbeck, M.: Extracting roadway background image: mode-based approach. Transp. Res. Rec. J. Transp. Res. Board 1944, 82\u201388 (2006)","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"issue":"3","key":"28_CR35","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1109\/TPAMI.2012.132","volume":"35","author":"X Zhou","year":"2013","unstructured":"Zhou, X., Yang, C., Yu, W.: Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 597\u2013610 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-4015-8_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,3,28]],"date-time":"2020-03-28T21:05:19Z","timestamp":1585429519000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-4015-8_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811540141","9789811540158"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-4015-8_28","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 March 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jaipur","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"27 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/cvip2019.mnit.ac.in\/","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":"202","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":"73","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":"10","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":"36% - 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":"5","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)"}}]}}