{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T22:03:45Z","timestamp":1730325825919,"version":"3.28.0"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","funder":[{"name":"Youth Innovation Fund Project of Xiamen City","award":["3502Z20206068"]},{"name":"Scientific Research Climbing Program of Xiamen University of Technology","award":["XPDKT19013"]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2021J011185"],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,3,18]]},"DOI":"10.1145\/3532213.3532276","type":"proceedings-article","created":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T09:29:18Z","timestamp":1657704558000},"page":"419-424","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Learning Based Multi-Target Multi-Camera Tracking System"],"prefix":"10.1145","author":[{"given":"Chenwei","family":"Dong","sequence":"first","affiliation":[{"name":"Xiamen University of Technology, China"}]},{"given":"Junlei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Xiamen University of Technology, China"}]},{"given":"Weipeng","family":"Wen","sequence":"additional","affiliation":[{"name":"Xiamen University of Technology, China"}]},{"given":"Si","family":"Chen","sequence":"additional","affiliation":[{"name":"Xiamen University of Technology, China"}]}],"member":"320","published-online":{"date-parts":[[2022,7,13]]},"reference":[{"volume-title":"An improved adaptive background mixture model for real-time tracking with shadow detection[M]\/\/Video-based surveillance systems","year":"2002","author":"KaewTraKulPong P","key":"e_1_3_2_1_1_1","unstructured":"KaewTraKulPong P , Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection[M]\/\/Video-based surveillance systems . Springer , Boston, MA , 2002 : 135-144. KaewTraKulPong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection[M]\/\/Video-based surveillance systems. Springer, Boston, MA, 2002: 135-144."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2101613"},{"volume-title":"A consensus-based method for tracking: Modelling background scenario and foreground appearance[J]. Pattern recognition","year":"2007","author":"Wang H","key":"e_1_3_2_1_3_1","unstructured":"Wang H , Suter D. A consensus-based method for tracking: Modelling background scenario and foreground appearance[J]. Pattern recognition , 2007 , 40(3): 1091-1105. Wang H, Suter D. A consensus-based method for tracking: Modelling background scenario and foreground appearance[J]. Pattern recognition, 2007, 40(3): 1091-1105."},{"volume-title":"Background segmentation with feedback: The pixel-based adaptive segmenter[C]\/\/2012 IEEE computer society conference on computer vision and pattern recognition workshops","year":"2012","author":"Hofmann M","key":"e_1_3_2_1_4_1","unstructured":"Hofmann M , Tiefenbacher P , Rigoll G. Background segmentation with feedback: The pixel-based adaptive segmenter[C]\/\/2012 IEEE computer society conference on computer vision and pattern recognition workshops . IEEE , 2012 : 38-43. Hofmann M, Tiefenbacher P, Rigoll G. Background segmentation with feedback: The pixel-based adaptive segmenter[C]\/\/2012 IEEE computer society conference on computer vision and pattern recognition workshops. IEEE, 2012: 38-43."},{"key":"e_1_3_2_1_5_1","first-page":"886","article-title":"Histograms of oriented gradients for human detection[C]\/\/2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05)","volume":"1","author":"Dalal N","year":"2005","unstructured":"Dalal N , Triggs B . Histograms of oriented gradients for human detection[C]\/\/2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) . Ieee , 2005 , 1 : 886 - 893 . Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]\/\/2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05). Ieee, 2005, 1: 886-893.","journal-title":"Ieee"},{"key":"e_1_3_2_1_6_1","first-page":"878","article-title":"Pedestrian detection in crowded scenes[C]\/\/2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)","volume":"1","author":"Leibe B","year":"2005","unstructured":"Leibe B , Seemann E , Schiele B . Pedestrian detection in crowded scenes[C]\/\/2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) . IEEE , 2005 , 1 : 878 - 885 . Leibe B, Seemann E, Schiele B. Pedestrian detection in crowded scenes[C]\/\/2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). IEEE, 2005, 1: 878-885.","journal-title":"IEEE"},{"volume-title":"Single shot multibox detector[C]\/\/European conference on computer vision","year":"2016","author":"Liu W","key":"e_1_3_2_1_7_1","unstructured":"Liu W , Anguelov D , Erhan D , Ssd : Single shot multibox detector[C]\/\/European conference on computer vision . Springer , Cham , 2016 : 21-37. Liu W, Anguelov D, Erhan D, Ssd : Single shot multibox detector[C]\/\/European conference on computer vision. Springer, Cham, 2016: 21-37."},{"volume-title":"Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv:2004.10934","year":"2020","author":"Bochkovskiy A","key":"e_1_3_2_1_8_1","unstructured":"Bochkovskiy A , Wang C Y , Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv:2004.10934 , 2020 . Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv:2004.10934, 2020."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Cai Z Vasconcelos N. Cascade r-cnn: Delving into high quality object detection[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 6154-6162. Cai Z Vasconcelos N. Cascade r-cnn: Delving into high quality object detection[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 6154-6162.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"e_1_3_2_1_10_1","first-page":"3464","article-title":"work was supported by the Natural Science Foundation of Fujian ProBewley A, Ge Z, Ott L, Simple online and realtime tracking[C]\/\/2016 IEEE international conference on image processing (ICIP)","volume":"2016","author":"This","unstructured":"ACKNOWLEDGMENT This work was supported by the Natural Science Foundation of Fujian ProBewley A, Ge Z, Ott L, Simple online and realtime tracking[C]\/\/2016 IEEE international conference on image processing (ICIP) . IEEE , 2016 : 3464 - 3468 . ACKNOWLEDGMENTThis work was supported by the Natural Science Foundation of Fujian ProBewley A, Ge Z, Ott L, Simple online and realtime tracking[C]\/\/2016 IEEE international conference on image processing (ICIP). IEEE, 2016: 3464-3468.","journal-title":"IEEE"},{"key":"e_1_3_2_1_11_1","first-page":"3645","article-title":"Simple online and realtime tracking with a deep association metric[C]\/\/2017 IEEE international conference on image processing (ICIP)","volume":"2017","author":"Wojke N","unstructured":"Wojke N , Bewley A , Paulus D . Simple online and realtime tracking with a deep association metric[C]\/\/2017 IEEE international conference on image processing (ICIP) . IEEE , 2017 : 3645 - 3649 . Wojke N, Bewley A, Paulus D. Simple online and realtime tracking with a deep association metric[C]\/\/2017 IEEE international conference on image processing (ICIP). IEEE, 2017: 3645-3649.","journal-title":"IEEE"},{"key":"e_1_3_2_1_12_1","first-page":"107","volume-title":"Proceedings, Part XI 16","author":"Wang Z","year":"2020","unstructured":"Wang Z , Zheng L , Liu Y , Towards real-time multi-object tracking[C]\/\/Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020 , Proceedings, Part XI 16 . Springer International Publishing , 2020 : 107 - 122 . Wang Z, Zheng L, Liu Y, Towards real-time multi-object tracking[C]\/\/Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XI 16. Springer International Publishing, 2020: 107-122."},{"key":"e_1_3_2_1_13_1","first-page":"1","article-title":"On the fairness of detection and re-identification in multiple object tracking[J]","volume":"2021","author":"Zhang Y","unstructured":"Zhang Y , Wang C , Wang X , Fairmot : On the fairness of detection and re-identification in multiple object tracking[J] . International Journal of Computer Vision , 2021 : 1 - 19 . Zhang Y, Wang C, Wang X, Fairmot: On the fairness of detection and re-identification in multiple object tracking[J]. International Journal of Computer Vision, 2021: 1-19.","journal-title":"International Journal of Computer Vision"},{"volume-title":"Deep transfer learning for person re-identification[J]. arXiv preprint arXiv:1611.05244","year":"2016","author":"Geng M","key":"e_1_3_2_1_14_1","unstructured":"Geng M , Wang Y , Xiang T , Deep transfer learning for person re-identification[J]. arXiv preprint arXiv:1611.05244 , 2016 . Geng M, Wang Y, Xiang T, Deep transfer learning for person re-identification[J]. arXiv preprint arXiv:1611.05244, 2016."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.06.006"},{"volume-title":"Person re-identification using cnn features learned from combination of attributes[C]\/\/2016 23rd international conference on pattern recognition (ICPR)","year":"2016","author":"Matsukawa T","key":"e_1_3_2_1_16_1","unstructured":"Matsukawa T , Suzuki E. Person re-identification using cnn features learned from combination of attributes[C]\/\/2016 23rd international conference on pattern recognition (ICPR) . IEEE , 2016 : 2428-2433. Matsukawa T, Suzuki E. Person re-identification using cnn features learned from combination of attributes[C]\/\/2016 23rd international conference on pattern recognition (ICPR). IEEE, 2016: 2428-2433."},{"volume-title":"Gated siamese convolutional neural network architecture for human re-identification[C]\/\/European conference on computer vision","year":"2016","author":"Varior R R","key":"e_1_3_2_1_17_1","unstructured":"Varior R R , Haloi M , Wang G. Gated siamese convolutional neural network architecture for human re-identification[C]\/\/European conference on computer vision . Springer , Cham , 2016 : 791-808. Varior R R, Haloi M, Wang G. Gated siamese convolutional neural network architecture for human re-identification[C]\/\/European conference on computer vision. Springer, Cham, 2016: 791-808."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Schroff F Kalenichenko D Philbin J. Facenet: A unified embedding for face recognition and clustering[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 815-823. Schroff F Kalenichenko D Philbin J. Facenet: A unified embedding for face recognition and clustering[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 815-823.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2700762"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Cheng D Gong Y Zhou S Person re-identification by multi-channel parts-based cnn with improved triplet loss function[C]\/\/Proceedings of the iEEE conference on computer vision and pattern recognition. 2016: 1335-1344. Cheng D Gong Y Zhou S Person re-identification by multi-channel parts-based cnn with improved triplet loss function[C]\/\/Proceedings of the iEEE conference on computer vision and pattern recognition. 2016: 1335-1344.","DOI":"10.1109\/CVPR.2016.149"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Chen W Chen X Zhang J Beyond triplet loss: a deep quadruplet network for person re-identification[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 403-412. Chen W Chen X Zhang J Beyond triplet loss: a deep quadruplet network for person re-identification[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 403-412.","DOI":"10.1109\/CVPR.2017.145"},{"volume-title":"Leibe B. In defense of the triplet loss for person re-identification[J]. arXiv preprint arXiv:1703.07737","year":"2017","author":"Hermans A","key":"e_1_3_2_1_22_1","unstructured":"Hermans A , Beyer L , Leibe B. In defense of the triplet loss for person re-identification[J]. arXiv preprint arXiv:1703.07737 , 2017 . Hermans A, Beyer L, Leibe B. In defense of the triplet loss for person re-identification[J]. arXiv preprint arXiv:1703.07737, 2017."},{"volume-title":"Margin sample mining loss: A deep learning based method for person re-identification[J]. arXiv preprint arXiv:1710.00478","year":"2017","author":"Xiao Q","key":"e_1_3_2_1_23_1","unstructured":"Xiao Q , Luo H , Zhang C. Margin sample mining loss: A deep learning based method for person re-identification[J]. arXiv preprint arXiv:1710.00478 , 2017 . Xiao Q, Luo H, Zhang C. Margin sample mining loss: A deep learning based method for person re-identification[J]. arXiv preprint arXiv:1710.00478, 2017."},{"volume-title":"mixup: Beyond empirical risk minimization[J]. arXiv preprint arXiv:1710.09412","year":"2017","author":"Zhang H","key":"e_1_3_2_1_24_1","unstructured":"Zhang H , Cisse M , Dauphin Y N , mixup: Beyond empirical risk minimization[J]. arXiv preprint arXiv:1710.09412 , 2017 . Zhang H, Cisse M, Dauphin Y N, mixup: Beyond empirical risk minimization[J]. arXiv preprint arXiv:1710.09412, 2017."},{"volume-title":"Mane D","year":"1805","author":"Cubuk E D","key":"e_1_3_2_1_25_1","unstructured":"Cubuk E D , Zoph B , Mane D , Autoaugment : Learning augmentation policies from data[J]. arXiv preprint arXiv: 1805 .09501, 2018. Cubuk E D, Zoph B, Mane D, Autoaugment: Learning augmentation policies from data[J]. arXiv preprint arXiv:1805.09501, 2018."},{"volume-title":"Gridmask data augmentation[J]. arXiv preprint arXiv:2001.04086","year":"2020","author":"Chen P","key":"e_1_3_2_1_26_1","unstructured":"Chen P , Liu S , Zhao H , Gridmask data augmentation[J]. arXiv preprint arXiv:2001.04086 , 2020 . Chen P, Liu S, Zhao H, Gridmask data augmentation[J]. arXiv preprint arXiv:2001.04086, 2020."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Hou Q Zhou D Feng J. Coordinate attention for efficient mobile network design[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2021: 13713-13722. Hou Q Zhou D Feng J. Coordinate attention for efficient mobile network design[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2021: 13713-13722.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"e_1_3_2_1_28_1","unstructured":"Zhou Z Tan S Xu Z M\u00f6bius Transformation for Fast Inner Product Search on Graph[C]\/\/NeurIPS. 2019: 8216-8227. Zhou Z Tan S Xu Z M\u00f6bius Transformation for Fast Inner Product Search on Graph[C]\/\/NeurIPS. 2019: 8216-8227."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"He K Zhang X Ren S Deep residual learning for image recognition[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778. He K Zhang X Ren S Deep residual learning for image recognition[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.","DOI":"10.1109\/CVPR.2016.90"}],"event":{"name":"ICCAI '22: 2022 8th International Conference on Computing and Artificial Intelligence","acronym":"ICCAI '22","location":"Tianjin China"},"container-title":["Proceedings of the 8th International Conference on Computing and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3532213.3532276","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T00:32:30Z","timestamp":1673569950000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3532213.3532276"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,18]]},"references-count":29,"alternative-id":["10.1145\/3532213.3532276","10.1145\/3532213"],"URL":"https:\/\/doi.org\/10.1145\/3532213.3532276","relation":{},"subject":[],"published":{"date-parts":[[2022,3,18]]},"assertion":[{"value":"2022-07-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}
