{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T04:14:12Z","timestamp":1750997652141,"version":"3.41.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319700892"},{"type":"electronic","value":"9783319700908"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-70090-8_29","type":"book-chapter","created":{"date-parts":[[2017,10,27]],"date-time":"2017-10-27T04:33:39Z","timestamp":1509078819000},"page":"280-290","source":"Crossref","is-referenced-by-count":1,"title":["Level Set Based Online Visual Tracking via Convolutional Neural Network"],"prefix":"10.1007","author":[{"given":"Xiaodong","family":"Ning","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixiong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,10,28]]},"reference":[{"issue":"4","key":"29_CR1","first-page":"478","volume":"4","author":"X Li","year":"2013","unstructured":"Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., Hengel, A.V.D.: A survey of appearance models in visual object tracking. ACM Trans. Intell. Syst. Technol. 4(4), 478\u2013488 (2013)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"5","key":"29_CR2","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TIP.2012.2236340","volume":"22","author":"W Hu","year":"2013","unstructured":"Hu, W., Zhou, X., Li, W., Luo, W., Zhang, X., Maybank, S.: Active contour-based visual tracking by integrating colors, shapes and motions. IEEE Trans. Image Process. 22(5), 1778\u20131792 (2013)","journal-title":"IEEE Trans. Image Process."},{"key":"29_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1007\/978-3-540-88688-4_61","volume-title":"Computer Vision \u2013 ECCV 2008","author":"C Bibby","year":"2008","unstructured":"Bibby, C., Reid, I.: Robust real-time visual tracking using pixel-wise posteriors. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5303, pp. 831\u2013844. Springer, Heidelberg (2008). doi: 10.1007\/978-3-540-88688-4_61"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Barbu, T.: Template matching based video tracking system using a novel n-step search algorithm and HOG features. In: International Conference on Neural Information Processing (ICONIP), Doha, pp. 328\u2013336 (2012)","DOI":"10.1007\/978-3-642-34500-5_39"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, pp. 4293\u20134302 (2016)","DOI":"10.1109\/CVPR.2016.465"},{"issue":"4","key":"29_CR6","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1109\/TED.2016.2529656","volume":"25","author":"K Zhang","year":"2016","unstructured":"Zhang, K., Liu, Q., Wu, Y., Yang, M.H.: Robust visual tracking via convolutional networks without training. IEEE Trans. Image Process. 25(4), 1779\u20131792 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"29_CR7","unstructured":"Hong, S., You, T., Kwak, S., Han, B.: Online tracking by learning discriminative saliency map with convolutional neural network. In: International Conference on Machine Learning, Lille, pp. 597\u2013606 (2015)"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition, Santiago, pp. 3119\u20133127 (2015)","DOI":"10.1109\/ICCV.2015.357"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Ma, C., Huang, J., Yang, X., Yang, M.: Hierarchical convolutional features for visual tracking. In: IEEE Conference on Computer Vision, Santiago, pp. 3074\u20133082 (2015)","DOI":"10.1109\/ICCV.2015.352"},{"issue":"3","key":"29_CR10","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1109\/TCSVT.2009.2013520","volume":"19","author":"Y Yeh","year":"2009","unstructured":"Yeh, Y., Hsu, C.: Online selection of tracking features using AdaBoost. IEEE Trans. Circ. Syst. Video Technol. 19(3), 442\u2013446 (2009)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Godec, M., Roth, P.M., Bischof, H.: Hough-based tracking of non-rigid objects. In: IEEE International Conference on Computer Vision, Barcelona, vol. 117, no. 10, pp. 81\u201388 (2011)","DOI":"10.1109\/ICCV.2011.6126228"},{"issue":"5","key":"29_CR12","doi-asserted-by":"crossref","first-page":"2368","DOI":"10.1109\/TIP.2017.2676346","volume":"26","author":"S Duffner","year":"2017","unstructured":"Duffner, S., Garcia, C.: Fast pixelwise adaptive visual tracking of non-rigid objects. IEEE Trans. Image Process. 26(5), 2368\u20132380 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"29_CR13","doi-asserted-by":"crossref","unstructured":"Duffner, S., Garcia, C.: PixelTrack: a fast adaptive algorithm for tracking non-rigid objects. In: IEEE International Conference on Computer Vision, Sydney, pp. 2480\u20132487 (2013)","DOI":"10.1109\/ICCV.2013.308"},{"issue":"2","key":"29_CR14","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1006\/jvci.1999.0442","volume":"11","author":"TF Chan","year":"2000","unstructured":"Chan, T.F., Sandberg, B.Y., Vese, L.A.: Active contours without edges for vector-valued images. J. Vis. Commun. Image Represent. 11(2), 130\u2013141 (2000)","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"4","key":"29_CR15","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.mri.2012.10.010","volume":"31","author":"L Liu","year":"2013","unstructured":"Liu, L., Zhang, Q., Wu, M., Li, W., Shang, F.: Adaptive segmentation of magnetic resonance images with intensity inhomogeneity using level set method. Magn. Reson. Imaging 31(4), 567\u2013574 (2013)","journal-title":"Magn. Reson. Imaging"},{"issue":"11","key":"29_CR16","doi-asserted-by":"crossref","first-page":"3386","DOI":"10.1109\/TIP.2015.2447213","volume":"24","author":"X Sun","year":"2015","unstructured":"Sun, X., Yao, H., Zhang, S., Li, D.: Non-rigid object contour tracking via a novel supervised level set model. IEEE Trans. Image Process. 24(11), 3386\u20133399 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"29_CR17","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 [cs.CV] (2014)"},{"issue":"10","key":"29_CR18","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1109\/TPAMI.2005.205","volume":"27","author":"RT Collins","year":"2005","unstructured":"Collins, R.T., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1631\u20131643 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"29_CR19","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/TPAMI.2007.35","volume":"29","author":"S Avidan","year":"2007","unstructured":"Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 261\u2013271 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"29_CR20","first-page":"1","volume":"100","author":"D Tsai","year":"2010","unstructured":"Tsai, D., Flagg, M., Rehg, J.M.: Motion coherent tracking with multi-label MRF optimization. Int. J. Comput. Vis. 100(2), 1\u201311 (2010)","journal-title":"Int. J. Comput. Vis."},{"key":"29_CR21","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.neucom.2017.05.028","volume":"266","author":"L Liu","year":"2017","unstructured":"Liu, L., Fan, S., Ning, X., Liao, L.: An efficient level set model with self-similarity for texture segmentation. Neurocomputing 266, 150\u2013164 (2017)","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-70090-8_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T19:24:58Z","timestamp":1750965898000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-70090-8_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319700892","9783319700908"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-70090-8_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]}}}