{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T19:18:27Z","timestamp":1743103107466,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819985548"},{"type":"electronic","value":"9789819985555"}],"license":[{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8555-5_31","type":"book-chapter","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T07:02:36Z","timestamp":1703660556000},"page":"392-404","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Salient Feature Enhanced Multi-object Tracking with\u00a0Soft-Sparse Attention in\u00a0Transformer"],"prefix":"10.1007","author":[{"given":"Caihua","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Qu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyi","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runze","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sichu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,28]]},"reference":[{"key":"31_CR1","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Meinhardt, T., Leal-Taix\u00e9, L.: Tracking without bells and whistles. Int. Conf. Comput. Vis. (2019)","DOI":"10.1109\/ICCV.2019.00103"},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Zhou, X., Cui, J., Qu, M.: An improved multi-object tracking algorithm for autonomous driving based on DeepSORT. In: ICITE (2022)","DOI":"10.1109\/ICITE56321.2022.10101388"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Wang, X.: Intelligent multi-camera video surveillance: a review. Pattern Recognit. Lett. 34(1), 3\u201319 (2013)","DOI":"10.1016\/j.patrec.2012.07.005"},{"key":"31_CR4","unstructured":"Uchiyama, H., Marchand, E.: Object detection and pose tracking for augmented reality: recent approaches. In: Proc (2012)"},{"key":"31_CR5","doi-asserted-by":"publisher","unstructured":"Wang, Z., Zheng, L., Liu, Y., Li, Y., Wang, S.: Towards real-time multi-object tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 107\u2013122. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58621-8_7","DOI":"10.1007\/978-3-030-58621-8_7"},{"key":"31_CR6","unstructured":"Zhang, Y., Wang, C., Wang, X., Zeng, W., Liu, W.: Fairmot: on the fairness of detection and re-identification in multiple object tracking. In: IJCV, pp. 1\u201319 (2021)"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Zeng, F., Dong, B., Zhang, Y., Wang, T., Zhang, X., Wei, Y.: MOTR: end-to-end multiple-object ttracking with transformer. In: ECCV (2022)","DOI":"10.1007\/978-3-031-19812-0_38"},{"key":"31_CR8","unstructured":"Sun, P., et al.: Transtrack: multiple-object tracking with transformer. arXiv preprint arXiv:2012.15460 (2020)"},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Meinhardt, T., Kirillov, A., Leal-Taix\u00e9, L., Feichtenhofer, C.: Trackformer: multi-object tracking with transformers. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00864"},{"key":"31_CR10","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurlPS (2017)"},{"key":"31_CR11","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: NIPS (2015)"},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"Xu, Y., Ban, Y., Delorme, G., Gan, C., Rus, D., Alameda-Pineda, X.: TransCenter: transformers with dense representations for multiple-object tracking. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)","DOI":"10.1109\/TPAMI.2022.3225078"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Fu, Z., Fu, Z., Liu, Q., Cai, W., Wang, Y.: SparseTT: visual tracking with sparse transformers. In: IJCA (2022)","DOI":"10.24963\/ijcai.2022\/127"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Chu, P., Wang, J., You, Q., Ling, H., Liu, Z.: TransMOT: spatial-temporal graph transformer for multiple object tracking. In: WACA (2023)","DOI":"10.1109\/WACV56688.2023.00485"},{"key":"31_CR15","doi-asserted-by":"crossref","unstructured":"Zhao, D., Zeng, Y.: Dynamic fusion of convolutional features based on spatial and temporal attention for visual tracking. In: IJCNN (2019)","DOI":"10.1109\/IJCNN.2019.8852301"},{"key":"31_CR16","doi-asserted-by":"crossref","unstructured":"Huang, D., Yang, M., Duan, J., Yu, S., Liu, Z.: Siamese network tracking based on feature enhancement. IEEE Access (2023)","DOI":"10.1109\/ACCESS.2023.3266264"},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"Bi, F., Sun, J., Lei, M., Wang, Y., Sun X.: Remote sensing target tracking for UAV aerial videos based on multi-frequency feature enhancement. In: IGARSS (2020)","DOI":"10.1109\/IGARSS39084.2020.9324283"},{"key":"31_CR18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"31_CR19","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: deformable transformers for end-to-end object detection. In: ICLR (2020)"},{"key":"31_CR20","unstructured":"Zhao, G., Lin, J., Zhang, Z., Ren, X., Su Q., Sun X.: Explicit sparse transformer: concentrated attention through explicit selection. arXiv preprint arXiv:1912.11637 (2019)"},{"key":"31_CR21","unstructured":"Milan, A., Leal-Taix\u00e9, L., Reid, I., Roth, S., Schindler, K.: Mot16: a benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831 (2016)"},{"key":"31_CR22","unstructured":"Dendorfer, P., et al.: Mot20: A benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003 (2020)"},{"key":"31_CR23","unstructured":"Shao, S., et al.: Crowdhuman: a benchmark for detecting human in a crowd. arXiv:1805.00123 (2018)"},{"key":"31_CR24","doi-asserted-by":"crossref","unstructured":"Hornakova, A., Henschel, R., Rosenhahn, B., Swoboda, P.: Lifted disjoint paths with application in multiple object tracking. In: International Conference on Machine Learning (2020)","DOI":"10.51202\/9783186875105-130"},{"key":"31_CR25","doi-asserted-by":"crossref","unstructured":"Zhou, X., Koltun, V., Krahenbuhl, P.: Tracking objects as points. In: ECCV, pp. 474\u2013490 (2020)","DOI":"10.1007\/978-3-030-58548-8_28"},{"key":"31_CR26","doi-asserted-by":"crossref","unstructured":"Lin, T., et al.: Microsoft coco: common objects in context. In: ECCV (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"31_CR27","unstructured":"Keuper, M., Tang, S., Andres, B., Brox, T., Schiele, B.: Motion segmentation and multiple object tracking by correlation co-clustering. IEEE Trans. Pattern Anal. Mach. Intell. (2018)"},{"key":"31_CR28","unstructured":"Henschel, R., Leal-Taix\u00e9, L., Cremers, D., Rosenhahn, B.: Improvements to Frank-Wolfe optimization for multi-detector multi-object tracking. In: CVPR (2017)"},{"key":"31_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: Long-term tracking with deep tracklet association. IEEE Trans. Image Process. (2020)","DOI":"10.1109\/TIP.2020.2993073"},{"key":"31_CR30","doi-asserted-by":"crossref","unstructured":"Braso, G., Leal-Taix\u00e9, L.: Learning a neural solver for multiple object tracking. IEEE Conf. Comput. Vis. Pattern Recogn. (2020)","DOI":"10.1109\/CVPR42600.2020.00628"},{"key":"31_CR31","doi-asserted-by":"crossref","unstructured":"Chu, P., Ling, H.: Famnet: joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking. In: CVPR (2019)","DOI":"10.1109\/ICCV.2019.00627"},{"key":"31_CR32","doi-asserted-by":"crossref","unstructured":"Liu, Q., Chu, Q., Liu, B., Yu, N.: GSM: Graph similarity model for multi-object tracking. Int. Joint Conf. Art. Int. (2020)","DOI":"10.24963\/ijcai.2020\/74"},{"key":"31_CR33","doi-asserted-by":"crossref","unstructured":"Wang, Y., Weng, X., Kitani, K.: Joint detection and multi-object tracking with graph neural networks. In: ICRA (2021)","DOI":"10.1109\/ICRA48506.2021.9561110"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8555-5_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T07:08:39Z","timestamp":1703660919000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8555-5_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,28]]},"ISBN":["9789819985548","9789819985555"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8555-5_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,28]]},"assertion":[{"value":"28 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","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 (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"532","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - 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 (provided by the conference organizers)"}},{"value":"3,78","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,69","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}