{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T01:44:47Z","timestamp":1775267087610,"version":"3.50.1"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030038007","type":"print"},{"value":"9783030038014","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-030-03801-4_30","type":"book-chapter","created":{"date-parts":[[2018,11,9]],"date-time":"2018-11-09T07:40:49Z","timestamp":1541749249000},"page":"332-343","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The Bird Gets Caught by the WORM: Tracking Multiple Deformable Objects in Noisy Environments Using Weight ORdered Logic Maps"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0020-5391","authenticated-orcid":false,"given":"Debajyoti","family":"Karmaker","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0044-1677","authenticated-orcid":false,"given":"Ingo","family":"Schiffner","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3115-9319","authenticated-orcid":false,"given":"Michael","family":"Wilson","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2875-4985","authenticated-orcid":false,"given":"Mandyam V.","family":"Srinivasan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,11,10]]},"reference":[{"issue":"8","key":"30_CR1","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1109\/TPAMI.2004.53","volume":"26","author":"S Avidan","year":"2004","unstructured":"Avidan, S.: Support vector tracking. IEEE Trans. Pattern Anal. Mach. Intell. 26(8), 1064\u20131072 (2004)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Benfold, B., Reid, I.: Stable multi-target tracking in real-time surveillance video. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3457\u20133464 (2011)","DOI":"10.1109\/CVPR.2011.5995667"},{"key":"30_CR3","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.neucom.2013.07.014","volume":"124","author":"X Cao","year":"2014","unstructured":"Cao, X., Gao, C., Lan, J., Yuan, Y., Yan, P.: Ego motion guided particle filter for vehicle tracking in airborne videos. Neurocomputing 124, 168\u2013177 (2014)","journal-title":"Neurocomputing"},{"issue":"2","key":"30_CR4","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1109\/TAES.1979.308710","volume":"15","author":"YT Chan","year":"1979","unstructured":"Chan, Y.T., Hu, A.G.C., Plant, J.B.: A Kalman filter based tracking scheme with input estimation. IEEE Trans. Aerosp. Electr. Syst. AES 15(2), 237\u2013244 (1979)","journal-title":"IEEE Trans. Aerosp. Electr. Syst. AES"},{"issue":"July","key":"30_CR5","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.measurement.2015.07.020","volume":"75","author":"IH Choi","year":"2015","unstructured":"Choi, I.H., Pak, J.M., Ahn, C.K., Lee, S.H., Lim, M.T., Song, M.K.: Arbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking. Meas. J. Int. Meas. Confederation 75(July), 338\u2013353 (2015)","journal-title":"Meas. J. Int. Meas. Confederation"},{"issue":"5","key":"30_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/34.1000236","volume":"24","author":"D Comaniciu","year":"2002","unstructured":"Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 1\u201337 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1007\/3-540-45053-X_48","volume-title":"Computer Vision \u2014 ECCV 2000","author":"A Elgammal","year":"2000","unstructured":"Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751\u2013767. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/3-540-45053-X_48"},{"key":"30_CR8","unstructured":"Everingham, M., Eslami, S.M.A., Gool, L.V., Williams, C.K.I., Winn, J., Zisserman, A.: Assessing the Significance of Performance Differences on the PASCAL VOC Challenges via Bootstrapping, vol. 1 (2013)"},{"issue":"9","key":"30_CR9","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627\u20131645 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"30_CR10","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.measurement.2012.01.004","volume":"45","author":"Z Fu","year":"2012","unstructured":"Fu, Z., Han, Y.: Centroid weighted Kalman filter for visual object tracking. Meas. J. Int. Meas. Confederation 45(4), 650\u2013655 (2012)","journal-title":"Meas. J. Int. Meas. Confederation"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"30_CR12","first-page":"543","volume":"3","author":"M Harville","year":"2002","unstructured":"Harville, M.: A framework for high-level feedback to adaptive, per-pixel, mixture-of-Gaussian background models. Framework 3, 543\u2013560 (2002)","journal-title":"Framework"},{"key":"30_CR13","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MOTION.2002.1182209","volume":"2002","author":"O Javed","year":"2002","unstructured":"Javed, O., Shafique, K., Shah, M.: A hierarchical approach to robust background subtraction using color and gradient information. Proc. Workshop Motion Video Comput. MOTION 2002, 22\u201327 (2002)","journal-title":"Proc. Workshop Motion Video Comput. MOTION"},{"key":"30_CR14","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances In Neural Information Processing Systems, pp. 1\u20139 (2012)"},{"issue":"5","key":"30_CR15","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1109\/TPAMI.2005.102","volume":"27","author":"DS Lee","year":"2005","unstructured":"Lee, D.S.: Effective Gaussian mixture learning for video background subtraction. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 827\u2013832 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"30_CR16","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from. Int. J. Comput. Vis. 60(2), 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vis."},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Mittal, A., Huttenlocher, D.: Scene modeling for wide area surveillance and image synthesis. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000 (Cat. No. PR00662), vol. 2, pp. 160\u2013167","DOI":"10.1109\/CVPR.2000.854767"},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"Monnet, A., Mittal, A., Paragios, N., Ramesh, V.R.V.: Background modeling and subtraction of dynamic scenes. In: Proceedings Ninth IEEE International Conference on Computer Vision (ICCV), vol. 2, pp. 1305\u20131312 (2003)","DOI":"10.1109\/ICCV.2003.1238641"},{"key":"30_CR19","unstructured":"Ridder, C., Munkelt, O., Kirchner, H.: Adaptive background estimation and foreground detection using Kalman-filtering. In: Proceedings of International Conference on Recent Advances in Mechatronics, pp. 193\u2013199 (1995)"},{"key":"30_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/978-3-642-33709-3_25","volume-title":"Computer Vision \u2013 ECCV 2012","author":"A Roshan Zamir","year":"2012","unstructured":"Roshan Zamir, A., Dehghan, A., Shah, M.: GMCP-tracker: global multi-object tracking using generalized minimum clique graphs. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, pp. 343\u2013356. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33709-3_25"},{"key":"30_CR21","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.asoc.2016.11.028","volume":"50","author":"F Sardari","year":"2017","unstructured":"Sardari, F., Ebrahimi Moghaddam, M.: A hybrid occlusion free object tracking method using particle filter and modified galaxy based search meta-heuristic algorithm. Appl. Soft Comput. J. 50, 280\u2013299 (2017)","journal-title":"Appl. Soft Comput. J."},{"key":"30_CR22","doi-asserted-by":"publisher","first-page":"10992","DOI":"10.1038\/srep10992","volume":"5","author":"I Schiffner","year":"2015","unstructured":"Schiffner, I., Srinivasan, M.V.: Direct evidence for vision-based control of flight speed in Budgerigars. Sci. Rep. 5, 10992 (2015). https:\/\/doi.org\/10.1038\/srep10992","journal-title":"Sci. Rep."},{"issue":"March","key":"30_CR23","first-page":"888","volume":"22","author":"J Shi","year":"2005","unstructured":"Shi, J., Malik, J.: Normalized cuts and image segmentation normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 22(March), 888\u2013905 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"key":"30_CR24","doi-asserted-by":"crossref","unstructured":"Solera, F., Calderara, S., Cucchiara, R.: Learning to divide and conquer for online multi-target tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4373\u20134381, 11\u201318 December 2016","DOI":"10.1109\/ICCV.2015.497"},{"issue":"4","key":"30_CR25","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.conb.2011.05.020","volume":"21","author":"MV Srinivasan","year":"2011","unstructured":"Srinivasan, M.V.: Visual control of navigation in insects and its relevance for robotics. Curr. Opin. Neurobiol. 21(4), 535\u2013543 (2011)","journal-title":"Curr. Opin. Neurobiol."},{"key":"30_CR26","doi-asserted-by":"crossref","unstructured":"Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Cat No PR00149, vol. 2(c), pp. 246\u2013252 (1999)","DOI":"10.1109\/CVPR.1999.784637"},{"key":"30_CR27","doi-asserted-by":"publisher","first-page":"27591","DOI":"10.1038\/srep27591","volume":"6","author":"HD Vo","year":"2016","unstructured":"Vo, H.D., Schiffner, I., Srinivasan, M.V.: Anticipatory manoeuvres in bird flight. Sci. Rep. 6, 27591 (2016). 27270506[pmid]","journal-title":"Sci. Rep."},{"key":"30_CR28","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1007\/978-3-642-15390-7_47","volume-title":"Knowledge-Based and Intelligent Information and Engineering Systems","author":"J Watada","year":"2010","unstructured":"Watada, J., Musa, Z., Jain, L.C., Fulcher, J.: Human tracking: a state-of-art survey. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds.) KES 2010. LNCS (LNAI), vol. 6277, pp. 454\u2013463. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15390-7_47"},{"issue":"8","key":"30_CR29","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1109\/TPAMI.2003.1217609","volume":"25","author":"J Weng","year":"2003","unstructured":"Weng, J., Zhang, Y., Hwang, W.S.: Candid covariance-free incremental principal component analysis. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 1034\u20131040 (2003)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"30_CR30","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1109\/34.598236","volume":"19","author":"C Wren","year":"1997","unstructured":"Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 780\u2013785 (1997)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"30_CR31","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","volume":"37","author":"Y Wu","year":"2015","unstructured":"Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834\u20131848 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"30_CR32","doi-asserted-by":"publisher","first-page":"13-es","DOI":"10.1145\/1177352.1177355","volume":"38","author":"A Yilmaz","year":"2006","unstructured":"Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 13-es (2006)","journal-title":"ACM Comput. Surv."},{"issue":"7","key":"30_CR33","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1587\/transinf.E93.D.1700","volume":"E93\u2013D","author":"K Yokoi","year":"2010","unstructured":"Yokoi, K.: Probabilistic BPRRC: robust change detection against illumination changes and background movements. IEICE Trans. Inf. Syst. E93\u2013D(7), 1700\u20131707 (2010)","journal-title":"IEICE Trans. Inf. Syst."},{"key":"30_CR34","doi-asserted-by":"crossref","unstructured":"Yokoyama, M., Poggio, T.: A contour-based moving object detection and tracking. In: 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, vol. (1), pp. 271\u2013276 (2005)","DOI":"10.1109\/VSPETS.2005.1570925"},{"issue":"8","key":"30_CR35","doi-asserted-by":"publisher","first-page":"2474","DOI":"10.1016\/j.patcog.2015.02.008","volume":"48","author":"S Zhang","year":"2015","unstructured":"Zhang, S., Sui, Y., Yu, X., Zhao, S., Zhang, L.: Hybrid support vector machines for robust object tracking. Pattern Recogn. 48(8), 2474\u20132488 (2015)","journal-title":"Pattern Recogn."},{"issue":"P1","key":"30_CR36","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1016\/j.neucom.2014.09.043","volume":"151","author":"X Zhong","year":"2015","unstructured":"Zhong, X., Zhong, X., Peng, X.: Robots visual servo control with features constraint employing Kalman-neural-network filtering scheme. Neurocomputing 151(P1), 268\u2013277 (2015)","journal-title":"Neurocomputing"},{"key":"30_CR37","doi-asserted-by":"crossref","unstructured":"Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 2(2), pp. 28\u201331 (2004)","DOI":"10.1109\/ICPR.2004.1333992"},{"issue":"7","key":"30_CR38","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1016\/j.patrec.2005.11.005","volume":"27","author":"Z Zivkovic","year":"2006","unstructured":"Zivkovic, Z., Heijden, F.V.D.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27(7), 773\u2013780 (2006)","journal-title":"Pattern Recogn. Lett."}],"container-title":["Lecture Notes in Computer Science","Advances in Visual Computing"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-03801-4_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:46:59Z","timestamp":1775263619000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-03801-4_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030038007","9783030038014"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-03801-4_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"ISVC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Visual Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Las Vegas, NV","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isvc2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isvc.net\/","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"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"113","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"66","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"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"}},{"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"}},{"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"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"54 papers were accepted for the main symposium out of 91 submissions; and 12 papers were accepted for the special tracks out of 22 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}