{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T04:14:40Z","timestamp":1752984880310,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Social welfare major project of Zhongshan","award":["2019B2010, 2019B2011"],"award-info":[{"award-number":["2019B2010, 2019B2011"]}]},{"name":"Achievement cultivation project of Zhongshan Industrial Technology Research Institute","award":["419N26"],"award-info":[{"award-number":["419N26"]}]},{"name":"Young innovative talents project of Education Department of Guangdong Province","award":["419YIY04"],"award-info":[{"award-number":["419YIY04"]}]},{"name":"Guangdong Basic and Applied Basic Research Projects","award":["2019A1515111082"],"award-info":[{"award-number":["2019A1515111082"]}]},{"name":"Fund for high level talents afforded by University of Electronic Science and Technology of China, Zhongshan Institute","award":["417YKQ12, 419YKQN15"],"award-info":[{"award-number":["417YKQ12, 419YKQN15"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1145\/3447587.3447589","type":"proceedings-article","created":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T13:52:18Z","timestamp":1622814738000},"page":"7-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Pedestrian Behavior Anomaly Detection Based on Dynamic Mode Decomposition and One-Class SVM"],"prefix":"10.1145","author":[{"given":"Zhang","family":"Weixi","sequence":"first","affiliation":[{"name":"School of Computer Guangdong University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Shuai","sequence":"additional","affiliation":[{"name":"Zhongshan Institute University of Electronic Science and Technology of China, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zou","family":"Kun","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Wensheng","sequence":"additional","affiliation":[{"name":"Zhongshan Institute University of Electronic Science and Technology of China, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,6,4]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5244\/C.22.99"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-005-1838-7"},{"volume-title":"Behavior recognition via sparse spatio-temporal features[C]\/\/2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance","year":"2005","author":"Doll\u00e1r P","key":"e_1_3_2_1_3_1"},{"key":"e_1_3_2_1_4_1","first-page":"2169","article-title":"Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories[C]\/\/2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"2","author":"Lazebnik S","year":"2006","journal-title":"IEEE"},{"key":"e_1_3_2_1_5_1","first-page":"1","article-title":"Fisher kernels on visual vocabularies for image categorization[C]\/\/2007 IEEE conference on computer vision and pattern recognition","volume":"2007","author":"Perronnin F","journal-title":"IEEE"},{"volume-title":"Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556","year":"2014","author":"Simonyan K","key":"e_1_3_2_1_6_1"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Zhang B Wang L Wang Z Real-time action recognition with enhanced motion vector CNNs[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2718-2726.  Zhang B Wang L Wang Z Real-time action recognition with enhanced motion vector CNNs[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2718-2726.","DOI":"10.1109\/CVPR.2016.297"},{"volume-title":"Long-term temporal convolutions for action recognition[J]","year":"2017","author":"Varol G","key":"e_1_3_2_1_8_1"},{"key":"e_1_3_2_1_9_1","unstructured":"Simonyan K Zisserman A. Two-stream convolutional networks for action recognition in videos[C]\/\/Advances in neural information processing systems. 2014: 568-576.  Simonyan K Zisserman A. Two-stream convolutional networks for action recognition in videos[C]\/\/Advances in neural information processing systems. 2014: 568-576."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Yue-Hei Ng J Hausknecht M Vijayanarasimhan S Beyond short snippets: Deep networks for video classification[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 4694-4702.  Yue-Hei Ng J Hausknecht M Vijayanarasimhan S Beyond short snippets: Deep networks for video classification[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 4694-4702.","DOI":"10.1109\/CVPR.2015.7299101"},{"volume-title":"Long short-term memory[J]. Neural computation","year":"1997","author":"Hochreiter S","key":"e_1_3_2_1_11_1"},{"volume-title":"Draw: A recurrent neural network for image generation[J]. arXiv preprint arXiv:1502.04623","year":"2015","author":"Gregor K","key":"e_1_3_2_1_12_1"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Donahue J Anne Hendricks L Guadarrama S Long-term recurrent convolutional networks for visual recognition and description[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 2625-2634.  Donahue J Anne Hendricks L Guadarrama S Long-term recurrent convolutional networks for visual recognition and description[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 2625-2634.","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"e_1_3_2_1_14_1","first-page":"4489","article-title":"Learning spatial temporal features with 3d convolutional networks[C].","volume":"2015","author":"Du Tran","journal-title":"International Conference on Computer Vision"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Chollet F. Xception: Deep learning with depthwise separable convolutions[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1251-1258.  Chollet F. Xception: Deep learning with depthwise separable convolutions[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1251-1258.","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_3_2_1_16_1","unstructured":"Du Y Wang W Wang L. Hierarchical recurrent neural network for skeleton based action recognition[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1110-1118.  Du Y Wang W Wang L. Hierarchical recurrent neural network for skeleton based action recognition[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1110-1118."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2896631"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2004.06.015"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11071-005-2824-x"},{"volume-title":"Dynamic mode decomposition of numerical and experimental data[J]. Journal of fluid mechanics","year":"2010","author":"Schmid P J","key":"e_1_3_2_1_20_1"},{"volume-title":"Spectral analysis of nonlinear flows[J]. Journal of fluid mechanics","year":"2009","author":"Rowley C W","key":"e_1_3_2_1_21_1"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/2566785"},{"volume-title":"Multi-resolution dynamic mode decomposition for foreground\/background separation and object tracking[C]\/\/2015 IEEE International Conference on Computer Vision Workshop (ICCVW)","year":"2015","author":"Kutz J N","key":"e_1_3_2_1_23_1"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1137\/15M1023543"},{"volume-title":"Support-vector networks[J]. Machine learning","year":"1995","author":"Cortes C","key":"e_1_3_2_1_25_1"},{"volume-title":"Duin R P W. Support vector domain description[J]. Pattern recognition letters","year":"1999","author":"Tax D M J","key":"e_1_3_2_1_26_1"}],"event":{"name":"ICIGP 2021: 2021 The 4th International Conference on Image and Graphics Processing","acronym":"ICIGP 2021","location":"Sanya China"},"container-title":["2021 The 4th International Conference on Image and Graphics Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447587.3447589","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447587.3447589","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:28:37Z","timestamp":1750195717000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447587.3447589"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":26,"alternative-id":["10.1145\/3447587.3447589","10.1145\/3447587"],"URL":"https:\/\/doi.org\/10.1145\/3447587.3447589","relation":{},"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-06-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}