{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:21:52Z","timestamp":1759335712674,"version":"3.40.3"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031200731"},{"type":"electronic","value":"9783031200748"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20074-8_2","type":"book-chapter","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T20:23:11Z","timestamp":1668198191000},"page":"20-37","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MOTCOM: The Multi-Object Tracking Dataset Complexity Metric"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2941-9150","authenticated-orcid":false,"given":"Malte","family":"Pedersen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0544-0422","authenticated-orcid":false,"given":"Joakim Bruslund","family":"Haurum","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4623-8749","authenticated-orcid":false,"given":"Patrick","family":"Dendorfer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7584-5209","authenticated-orcid":false,"given":"Thomas B.","family":"Moeslund","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","unstructured":"Andriyenko, A., Roth, S., Schindler, K.: An analytical formulation of global occlusion reasoning for multi-target tracking. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1839\u20131846. IEEE (2011). https:\/\/doi.org\/10.1109\/ICCVW.2011.6130472","DOI":"10.1109\/ICCVW.2011.6130472"},{"key":"2_CR2","doi-asserted-by":"publisher","unstructured":"Andriyenko, A., Schindler, K.: Multi-target tracking by continuous energy minimization. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1265\u20131272 (2011). https:\/\/doi.org\/10.1109\/CVPR.2011.5995311","DOI":"10.1109\/CVPR.2011.5995311"},{"key":"2_CR3","doi-asserted-by":"publisher","unstructured":"Bergmann, P., Meinhardt, T., Leal-Taix\u00e9, L.: Tracking without bells and whistles. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 941\u2013951 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00103","DOI":"10.1109\/ICCV.2019.00103"},{"key":"2_CR4","doi-asserted-by":"publisher","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464\u20133468 (2016). https:\/\/doi.org\/10.1109\/ICIP.2016.7533003","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Branchaud-Charron, F., Achkar, A., Jodoin, P.M.: Spectral metric for dataset complexity assessment. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3210\u20133219 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00333","DOI":"10.1109\/CVPR.2019.00333"},{"key":"2_CR6","doi-asserted-by":"publisher","unstructured":"Cao, X., Guo, S., Lin, J., Zhang, W., Liao, M.: Online tracking of ants based on deep association metrics: method, dataset and evaluation. Pattern Recogn. 103 (2020). https:\/\/doi.org\/10.1016\/j.patcog.2020.107233","DOI":"10.1016\/j.patcog.2020.107233"},{"key":"2_CR7","doi-asserted-by":"publisher","unstructured":"Chang, M.F., et al.: Argoverse: 3D tracking and forecasting with rich maps. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8740\u20138749 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00895","DOI":"10.1109\/CVPR.2019.00895"},{"key":"2_CR8","doi-asserted-by":"publisher","unstructured":"Cui, Y., Gu, Z., Mahajan, D., van der Maaten, L., Belongie, S., Lim, S.N.: Measuring dataset granularity (2019). https:\/\/doi.org\/10.48550\/ARXIV.1912.10154","DOI":"10.48550\/ARXIV.1912.10154"},{"issue":"4","key":"2_CR9","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1007\/s11263-020-01393-0","volume":"129","author":"P Dendorfer","year":"2020","unstructured":"Dendorfer, P., et al.: MOTChallenge: a benchmark for single-camera multiple target tracking. Int. J. Comput. Vision 129(4), 845\u2013881 (2020). https:\/\/doi.org\/10.1007\/s11263-020-01393-0","journal-title":"Int. J. Comput. Vision"},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248\u2013255 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"2","key":"2_CR11","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1111\/j.2517-6161.1977.tb01624.x","volume":"39","author":"P Diaconis","year":"1977","unstructured":"Diaconis, P., Graham, R.L.: Spearman\u2019s footrule as a measure of disarray. J. Roy. Stat. Soc.: Ser. B (Methodol.) 39(2), 262\u2013268 (1977). https:\/\/doi.org\/10.1111\/j.2517-6161.1977.tb01624.x","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"key":"2_CR12","doi-asserted-by":"publisher","unstructured":"Fabbri, M., et al.: Motsynth: how can synthetic data help pedestrian detection and tracking? In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10829\u201310839 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.01067","DOI":"10.1109\/ICCV48922.2021.01067"},{"issue":"1","key":"2_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41074-017-0038-z","volume":"10","author":"R Gade","year":"2018","unstructured":"Gade, R., Moeslund, T.B.: Constrained multi-target tracking for team sports activities. IPSJ Trans. Comput. Vision Appl. 10(1), 1\u201311 (2018). https:\/\/doi.org\/10.1186\/s41074-017-0038-z","journal-title":"IPSJ Trans. Comput. Vision Appl."},{"key":"2_CR14","doi-asserted-by":"publisher","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354\u20133361 (2012). https:\/\/doi.org\/10.1109\/CVPR.2012.6248074","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"2_CR15","doi-asserted-by":"publisher","unstructured":"Haurum, J.B., Karpova, A., Pedersen, M., Bengtson, S.H., Moeslund, T.B.: Re-identification of zebrafish using metric learning. In: 2020 IEEE Winter Applications of Computer Vision Workshops (WACVW), pp. 1\u201311 (2020). https:\/\/doi.org\/10.1109\/WACVW50321.2020.9096922","DOI":"10.1109\/WACVW50321.2020.9096922"},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"2_CR17","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1109\/34.990132","volume":"24","author":"TK Ho","year":"2002","unstructured":"Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 24(3), 289\u2013300 (2002). https:\/\/doi.org\/10.1109\/34.990132","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"key":"2_CR18","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.cviu.2019.03.001","volume":"182","author":"SD Khan","year":"2019","unstructured":"Khan, S.D., Ullah, H.: A survey of advances in vision-based vehicle re-identification. Comput. Vis. Image Underst. 182, 50\u201363 (2019). https:\/\/doi.org\/10.1016\/j.cviu.2019.03.001","journal-title":"Comput. Vis. Image Underst."},{"key":"2_CR19","doi-asserted-by":"publisher","unstructured":"Kratz, L., Nishino, K.: Tracking with local spatio-temporal motion patterns in extremely crowded scenes. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 693\u2013700 (2010). https:\/\/doi.org\/10.1109\/CVPR.2010.5540149","DOI":"10.1109\/CVPR.2010.5540149"},{"key":"2_CR20","doi-asserted-by":"publisher","unstructured":"Leal-Taix\u00e9, L., Milan, A., Schindler, K., Cremers, D., Reid, I., Roth, S.: Tracking the trackers: an analysis of the state of the art in multiple object tracking. arXiv (2017). https:\/\/doi.org\/10.48550\/ARXIV.1704.02781","DOI":"10.48550\/ARXIV.1704.02781"},{"key":"2_CR21","doi-asserted-by":"publisher","unstructured":"Leal-Taix\u00e9, L., Canton-Ferrer, C., Schindler, K.: Learning by tracking: Siamese cnn for robust target association. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 418\u2013425 (2016). https:\/\/doi.org\/10.1109\/CVPRW.2016.59","DOI":"10.1109\/CVPRW.2016.59"},{"key":"2_CR22","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1109\/TIP.2019.2928123","volume":"29","author":"C Liu","year":"2020","unstructured":"Liu, C., Yao, R., Rezatofighi, S.H., Reid, I., Shi, Q.: Model-free tracker for multiple objects using joint appearance and motion inference. IEEE Trans. Image Process. 29, 277\u2013288 (2020). https:\/\/doi.org\/10.1109\/TIP.2019.2928123","journal-title":"IEEE Trans. Image Process."},{"key":"2_CR23","doi-asserted-by":"publisher","unstructured":"Lu, Z., Rathod, V., Votel, R., Huang, J.: Retinatrack: online single stage joint detection and tracking. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14656\u201314666 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01468","DOI":"10.1109\/CVPR42600.2020.01468"},{"key":"2_CR24","doi-asserted-by":"publisher","unstructured":"Luiten, J., Osep, A., Dendorfer, P., Torr, P., Geiger, A., Leal-Taix\u00e9, L., Leibe, B.: Hota: a higher order metric for evaluating multi-object tracking. International Journal of Computer Vision (IJCV), pp. 548\u2013578 (2021). https:\/\/doi.org\/10.1007\/s11263-020-01375-2","DOI":"10.1007\/s11263-020-01375-2"},{"key":"2_CR25","doi-asserted-by":"publisher","unstructured":"Luo, W., Kim, T.K., Stenger, B., Zhao, X., Cipolla, R.: Bi-label propagation for generic multiple object tracking. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1290\u20131297 (2014). https:\/\/doi.org\/10.1109\/CVPR.2014.168","DOI":"10.1109\/CVPR.2014.168"},{"key":"2_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2020.103448","volume":"293","author":"W Luo","year":"2021","unstructured":"Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., Kim, T.K.: Multiple object tracking: a literature review. Artif. Intell. 293, 103448 (2021). https:\/\/doi.org\/10.1016\/j.artint.2020.103448","journal-title":"Artif. Intell."},{"key":"2_CR27","doi-asserted-by":"publisher","unstructured":"Milan, A., Leal-Taix\u00e9, L., Reid, I., Roth, S., Schindler, K.: Mot16: a benchmark for multi-object tracking. arXiv (2016).https:\/\/doi.org\/10.48550\/ARXIV.1603.00831","DOI":"10.48550\/ARXIV.1603.00831"},{"issue":"1","key":"2_CR28","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/TPAMI.2013.103","volume":"36","author":"A Milan","year":"2014","unstructured":"Milan, A., Roth, S., Schindler, K.: Continuous energy minimization for multitarget tracking. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 36(1), 58\u201372 (2014). https:\/\/doi.org\/10.1109\/TPAMI.2013.103","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"key":"2_CR29","doi-asserted-by":"publisher","unstructured":"Milan, A., Schindler, K., Roth, S.: Challenges of ground truth evaluation of multi-target tracking. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 735\u2013742 (2013). https:\/\/doi.org\/10.1109\/CVPRW.2013.111","DOI":"10.1109\/CVPRW.2013.111"},{"key":"2_CR30","doi-asserted-by":"publisher","unstructured":"Pang, J., et al.: Quasi-dense similarity learning for multiple object tracking. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 164\u2013173 (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.00023","DOI":"10.1109\/CVPR46437.2021.00023"},{"key":"2_CR31","doi-asserted-by":"publisher","unstructured":"Pedersen, M., Haurum, J.B., Hein Bengtson, S., Moeslund, T.B.: 3D-ZEF: a 3D zebrafish tracking benchmark dataset. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2423\u20132433 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00250","DOI":"10.1109\/CVPR42600.2020.00250"},{"key":"2_CR32","doi-asserted-by":"publisher","unstructured":"Pellegrini, S., Ess, A., Schindler, K., van Gool, L.: You\u2019ll never walk alone: modeling social behavior for multi-target tracking. In: 2009 IEEE 12th International Conference on Computer Vision (ICCV), pp. 261\u2013268 (2009). https:\/\/doi.org\/10.1109\/ICCV.2009.5459260","DOI":"10.1109\/ICCV.2009.5459260"},{"key":"2_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/978-3-030-58548-8_9","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Peng","year":"2020","unstructured":"Peng, J., et al.: Chained-tracker: chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 145\u2013161. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58548-8_9"},{"issue":"7","key":"2_CR34","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1038\/nmeth.2994","volume":"11","author":"A P\u00e9rez-Escudero","year":"2014","unstructured":"P\u00e9rez-Escudero, A., Vicente-Page, J., Hinz, R.C., Arganda, S., De Polavieja, G.G.: idtracker: tracking individuals in a group by automatic identification of unmarked animals. Nat. Methods 11(7), 743\u2013748 (2014). https:\/\/doi.org\/10.1038\/nmeth.2994","journal-title":"Nat. Methods"},{"key":"2_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-3-319-48881-3_2","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"E Ristani","year":"2016","unstructured":"Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for\u00a0multi-target, multi-camera tracking. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17\u201335. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48881-3_2"},{"key":"2_CR36","doi-asserted-by":"publisher","unstructured":"Schneider, S., Taylor, G.W., Kremer, S.C.: Similarity learning networks for animal individual re-identification - beyond the capabilities of a human observer. In: 2020 IEEE Winter Applications of Computer Vision Workshops (WACVW), pp. 44\u201352 (2020). https:\/\/doi.org\/10.1109\/WACVW50321.2020.9096925","DOI":"10.1109\/WACVW50321.2020.9096925"},{"issue":"4","key":"2_CR37","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1111\/2041-210X.13133","volume":"10","author":"S Schneider","year":"2019","unstructured":"Schneider, S., Taylor, G.W., Linquist, S., Kremer, S.C.: Past, present and future approaches using computer vision for animal re-identification from camera trap data. Methods Ecol. Evol. 10(4), 461\u2013470 (2019). https:\/\/doi.org\/10.1111\/2041-210X.13133","journal-title":"Methods Ecol. Evol."},{"key":"2_CR38","doi-asserted-by":"publisher","unstructured":"Stadler, D., Beyerer, J.: Improving multiple pedestrian tracking by track management and occlusion handling. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10953\u201310962 (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.01081","DOI":"10.1109\/CVPR46437.2021.01081"},{"key":"2_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-540-69568-4_1","volume-title":"Multimodal Technologies for Perception of Humans","author":"R Stiefelhagen","year":"2007","unstructured":"Stiefelhagen, R., Bernardin, K., Bowers, R., Garofolo, J., Mostefa, D., Soundararajan, P.: The CLEAR 2006 evaluation. In: Stiefelhagen, R., Garofolo, J. (eds.) CLEAR 2006. LNCS, vol. 4122, pp. 1\u201344. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-69568-4_1"},{"key":"2_CR40","doi-asserted-by":"publisher","unstructured":"Sun, P., et al.: Scalability in perception for autonomous driving: waymo open dataset. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2443\u20132451 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00252","DOI":"10.1109\/CVPR42600.2020.00252"},{"issue":"2","key":"2_CR41","first-page":"128","volume":"80","author":"JK Uhlmann","year":"1992","unstructured":"Uhlmann, J.K.: Algorithms for multiple-target tracking. Am. Sci. 80(2), 128\u2013141 (1992)","journal-title":"Am. Sci."},{"key":"2_CR42","doi-asserted-by":"publisher","unstructured":"Voigtlaender, P., et al.: Mots: multi-object tracking and segmentation. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7934\u20137943 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00813","DOI":"10.1109\/CVPR.2019.00813"},{"key":"2_CR43","doi-asserted-by":"publisher","unstructured":"Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645\u20133649 (2017). https:\/\/doi.org\/10.1109\/ICIP.2017.8296962","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"2_CR44","doi-asserted-by":"publisher","unstructured":"Xiang, Y., Alahi, A., Savarese, S.: Learning to track: online multi-object tracking by decision making. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4705\u20134713 (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.534","DOI":"10.1109\/ICCV.2015.534"},{"key":"2_CR45","doi-asserted-by":"publisher","unstructured":"Xu, J., Cao, Y., Zhang, Z., Hu, H.: Spatial-temporal relation networks for multi-object tracking. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3987\u20133997 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00409","DOI":"10.1109\/ICCV.2019.00409"},{"issue":"6","key":"2_CR46","doi-asserted-by":"publisher","first-page":"2872","DOI":"10.1109\/TPAMI.2021.3054775","volume":"44","author":"M Ye","year":"2022","unstructured":"Ye, M., Shen, J., Lin, G., Xiang, T., Shao, L., Hoi, S.C.H.: Deep learning for person re-identification: a survey and outlook. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 44(6), 2872\u20132893 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3054775","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"key":"2_CR47","doi-asserted-by":"publisher","unstructured":"Yin, J., Wang, W., Meng, Q., Yang, R., Shen, J.: A unified object motion and affinity model for online multi-object tracking. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6767\u20136776 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00680","DOI":"10.1109\/CVPR42600.2020.00680"},{"issue":"11","key":"2_CR48","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.1007\/s11263-021-01513-4","volume":"129","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Wang, C., Wang, X., Zeng, W., Liu, W.: FairMOT: on the fairness of detection and re-identification in multiple object tracking. Int. J. Comput. Vision 129(11), 3069\u20133087 (2021). https:\/\/doi.org\/10.1007\/s11263-021-01513-4","journal-title":"Int. J. Comput. Vision"},{"key":"2_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1007\/978-3-030-58548-8_28","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Koltun, V., Kr\u00e4henb\u00fchl, P.: Tracking objects as points. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 474\u2013490. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58548-8_28"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20074-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T20:23:39Z","timestamp":1668198219000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20074-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200731","9783031200748"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20074-8_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"12 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}