{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:28:21Z","timestamp":1750220901354,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,12,6]],"date-time":"2019-12-06T00:00:00Z","timestamp":1575590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,12,6]]},"DOI":"10.1145\/3374587.3374615","type":"proceedings-article","created":{"date-parts":[[2020,3,4]],"date-time":"2020-03-04T18:16:31Z","timestamp":1583345791000},"page":"165-170","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Visual Tracking by Gated PixelCNN Model"],"prefix":"10.1145","author":[{"given":"Xia","family":"Xue","sequence":"first","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University (NCEPU), Beijing, China"}]},{"given":"Jingping","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University (NCEPU), Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2020,3,4]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.09.022"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.05.007"},{"issue":"2","key":"e_1_3_2_1_4_1","first-page":"023008","article-title":"Robust visual tracking based on deep convolutional neural networks and kernelized correlation fifilters","volume":"27","author":"Yang H.","unstructured":"H. Yang , D. Zhong , C. Liu , K. Song , and Z. Yin . Robust visual tracking based on deep convolutional neural networks and kernelized correlation fifilters . Journal of Electronic Imaging , 27 ( 2 ): 023008 . H. Yang, D. Zhong, C. Liu, K. Song, and Z. Yin. Robust visual tracking based on deep convolutional neural networks and kernelized correlation fifilters. Journal of Electronic Imaging, 27(2):023008.","journal-title":"Journal of Electronic Imaging"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2017.08.010"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_1_7_1","volume-title":"Learning multi-domain convolutionalneural networks for visual tracking. arXiv preprint arXiv:1510.07945","author":"Nam H.","year":"2015","unstructured":"H. Nam and B. Han . Learning multi-domain convolutionalneural networks for visual tracking. arXiv preprint arXiv:1510.07945 , 2015 . 1, 2, 4, 6, 7, 8 H. Nam and B. Han. Learning multi-domain convolutionalneural networks for visual tracking. arXiv preprint arXiv:1510.07945, 2015. 1, 2, 4, 6, 7, 8"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_9_1","first-page":"4790","volume-title":"Advances in neural information processing systems","author":"Van den Oord A.","year":"2016","unstructured":"A. Van den Oord , N. Kalchbrenner , L. Espeholt , O. Vinyals , A. Graves , Conditional image generation with pixelcnn decoders . In Advances in neural information processing systems , pages 4790 -- 4798 , 2016 . A. Van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves, et al. Conditional image generation with pixelcnn decoders. In Advances in neural information processing systems, pages 4790--4798, 2016."},{"key":"e_1_3_2_1_10_1","volume-title":"N. Kalchbrenner, and K. Kavukcuoglu. Pixel recurrent neural net works. arXiv preprint arXiv:1601.06759","author":"A.","year":"2016","unstructured":"A. v. d. Oord , N. Kalchbrenner, and K. Kavukcuoglu. Pixel recurrent neural net works. arXiv preprint arXiv:1601.06759 , 2016 . A. v. d. Oord, N. Kalchbrenner, and K. Kavukcuoglu. Pixel recurrent neural net works. arXiv preprint arXiv:1601.06759, 2016."},{"key":"e_1_3_2_1_11_1","volume-title":"Learning Multi-Domain Convolutional Neural Networks for Visual Tracking[J]","author":"B.","year":"2015","unstructured":"Nam H, Han B. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking[J] . 2015 . Nam H, Han B. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking[J]. 2015."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008935410038"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781119125495"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.312"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2694219"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2354884"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995733"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-007-0075-7"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247878"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2003.1211475"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_9"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33712-3_62"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"e_1_3_2_1_25_1","volume-title":"Tracking-learning-detection","author":"Kalal Z.","year":"2011","unstructured":"Z. Kalal , K. Mikolajczyk , and J. Matas . Tracking-learning-detection . IEEE transactions on pattern analysis and machine intelligence, 34(7):1409--1422, 2011 . Z. Kalal, K. Mikolajczyk, and J. Matas. Tracking-learning-detection. IEEE transactions on pattern analysis and machine intelligence, 34(7):1409--1422, 2011."},{"key":"e_1_3_2_1_26_1","volume-title":"Robust object tracking with online multiple instance learning","author":"Babenko B.","year":"2010","unstructured":"B. Babenko , M.-H. Yang , and S. Belongie . Robust object tracking with online multiple instance learning . IEEE transactions on pattern analysis and machine intelligence, 33(8):1619--1632, 2010 . B. Babenko, M.-H. Yang, and S. Belongie. Robust object tracking with online multiple instance learning. IEEE transactions on pattern analysis and machine intelligence, 33(8):1619--1632, 2010."}],"event":{"name":"CSAI2019: 2019 3rd International Conference on Computer Science and Artificial Intelligence","sponsor":["Shenzhen University Shenzhen University"],"location":"Normal IL USA","acronym":"CSAI2019"},"container-title":["Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3374587.3374615","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3374587.3374615","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:44:44Z","timestamp":1750203884000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3374587.3374615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,6]]},"references-count":26,"alternative-id":["10.1145\/3374587.3374615","10.1145\/3374587"],"URL":"https:\/\/doi.org\/10.1145\/3374587.3374615","relation":{},"subject":[],"published":{"date-parts":[[2019,12,6]]},"assertion":[{"value":"2020-03-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}