{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T01:45:14Z","timestamp":1773193514540,"version":"3.50.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031133237","type":"print"},{"value":"9783031133244","type":"electronic"}],"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\/978-3-031-13324-4_9","type":"book-chapter","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T20:21:50Z","timestamp":1659558110000},"page":"95-106","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Event-Based Object Detection and Tracking - A Traffic Monitoring Use Case\u00a0-"],"prefix":"10.1007","author":[{"given":"Simone","family":"Mentasti","sequence":"first","affiliation":[]},{"given":"Abednego Wamuhindo","family":"Kambale","sequence":"additional","affiliation":[]},{"given":"Matteo","family":"Matteucci","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Gallego. G., et al: Event-based vision: a survey. IEEE Trans. Patt. Anal. Mach. Intell. 44 154\u2013180 (2019)","DOI":"10.1109\/TPAMI.2020.3008413"},{"issue":"2","key":"9_CR2","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1177\/0278364917691115","volume":"36","author":"E Mueggler","year":"2017","unstructured":"Mueggler, E., Rebecq, H., Gallego, G., Delbruck, T., Scaramuzza, D.: The event-camera dataset and simulator: event-based data for pose estimation, visual odometry, and slam. Int. J. Robot. Res. 36(2), 142\u2013149 (2017)","journal-title":"Int. J. Robot. Res."},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Weikersdorfer, D., Adrian, D.B, Cremers, D., Conradt, J.: Event-based 3D slam with a depth-augmented dynamic vision sensor. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 359\u2013364. IEEE (2014)","DOI":"10.1109\/ICRA.2014.6906882"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Cannici, M., Ciccone, M., Romanoni, A., Matteucci, M.: Asynchronous convolutional networks for object detection in neuromorphic cameras. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 1656\u20131665 (2019)","DOI":"10.1109\/CVPRW.2019.00209"},{"key":"9_CR5","unstructured":"PROPHESEE. prophesee.ai website (2021)"},{"key":"9_CR6","unstructured":"Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M: YOLOV4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)"},{"issue":"1","key":"9_CR7","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1115\/1.3662552","volume":"82","author":"RE Kalman","year":"1960","unstructured":"Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35\u201345 (1960)","journal-title":"J. Basic Eng."},{"key":"9_CR8","unstructured":"Mills-Tettey, A., Stentz, A., Dias, M.B.: The dynamic Hungarian algorithm for the assignment problem with changing costs. Robotics Institute, Pittsburgh, PA, Technical report, CMU-RI-TR-07-27 (2007)"},{"key":"9_CR9","unstructured":"Boettiger, J.P.: A comparative evaluation of the detection and tracking capability between novel event-based and conventional frame-based sensors (2020)"},{"key":"9_CR10","doi-asserted-by":"publisher","first-page":"118","DOI":"10.3389\/fnins.2018.00118","volume":"12","author":"V Padala","year":"2018","unstructured":"Padala, V., Basu, A., Orchard, G.: A noise filtering algorithm for event-based asynchronous change detection image sensors on TrueNorth and its implementation on TrueNorth. Front. Neurosci. 12, 118 (2018)","journal-title":"Front. Neurosci."},{"issue":"2","key":"9_CR11","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/s11554-018-0756-1","volume":"17","author":"F Bolelli","year":"2020","unstructured":"Bolelli, F., Cancilla, M., Baraldi, L., Grana, C.: Toward reliable experiments on the performance of connected components labeling algorithms. J. Real-Time Image Proc. 17(2), 229\u2013244 (2020)","journal-title":"J. Real-Time Image Proc."},{"key":"9_CR12","first-page":"1","volume":"19","author":"LG Shapiro","year":"1996","unstructured":"Shapiro, L.G.: Connected component labeling and adjacency graph construction. Mach. Intell. Pattern Recogn. 19, 1\u201330 (1996)","journal-title":"Mach. Intell. Pattern Recogn."},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Lemaitre, F., Hennequin, A., Lacassagne, L.: Taming voting algorithms on GPUs for an efficient connected component analysis algorithm. In: ICASSP 2021\u20132021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7903\u20137907. IEEE (2021)","DOI":"10.1109\/ICASSP39728.2021.9413653"},{"key":"9_CR14","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.patcog.2017.04.018","volume":"70","author":"L He","year":"2017","unstructured":"He, L., Ren, X., Gao, Q., Zhao, X., Yao, B., Chao, Y.: The connected-component labeling problem: a review of state-of-the-art algorithms. Pattern Recogn. 70, 25\u201343 (2017)","journal-title":"Pattern Recogn."},{"key":"9_CR15","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1109\/TIP.2019.2946979","volume":"29","author":"F Bolelli","year":"2019","unstructured":"Bolelli, F., Allegretti, S., Baraldi, L., Grana, C.: Spaghetti labeling: directed acyclic graphs for block-based connected components labeling. IEEE Trans. Image Process. 29, 1999\u20132012 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"9_CR16","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","volume":"13","author":"B Scholkopf","year":"1998","unstructured":"Scholkopf, B.: Support vector machines: a practical consequence of learning theory. IEEE Intell. Syst. 13, 18\u201328 (1998)","journal-title":"IEEE Intell. Syst."},{"issue":"1","key":"9_CR17","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119\u2013139 (1997)","journal-title":"J. Comput. Syst. Sci."},{"key":"9_CR18","doi-asserted-by":"publisher","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","volume":"30","author":"Z-Q Zhao","year":"2018","unstructured":"Zhao, Z.-Q., Zheng, P., Xu, S.T., Wu, X.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30, 3212\u20133232 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Shafiee, M.J., Chywl, B., Li, F., Wong, A.: Fast Yolo: a fast you only look once system for real-time embedded object detection in video. arXiv preprint arXiv:1709.05943 (2017)","DOI":"10.15353\/vsnl.v3i1.171"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 6517\u20136525 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"issue":"1\u20132","key":"9_CR22","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1002\/nav.3800020109","volume":"2","author":"HW Kuhn","year":"1955","unstructured":"Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logistics Q. 2(1\u20132), 83\u201397 (1955)","journal-title":"Naval Res. Logistics Q."},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Mitrokhin, A., Ferm\u00fcller, C., Parameshwara, C., Aloimonos, Y.: Event-based moving object detection and tracking. In: 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1\u20139. IEEE (2018)","DOI":"10.1109\/IROS.2018.8593805"},{"key":"9_CR24","unstructured":"Br\u00e4ndli, C.: Event-based machine vision. Ph.D. thesis, ETH Zurich (2015)"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Bagchi, S., Chin, T.-J.: Event-based star tracking via multiresolution progressive Hough transforms. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2143\u20132152 (2020)","DOI":"10.1109\/WACV45572.2020.9093309"},{"key":"9_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/978-3-030-58565-5_9","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M Cannici","year":"2020","unstructured":"Cannici, M., Ciccone, M., Romanoni, A., Matteucci, M.: A differentiable recurrent surface for asynchronous event-based data. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 136\u2013152. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58565-5_9"},{"issue":"1","key":"9_CR27","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/0734-189X(85)90016-7","volume":"30","author":"S Suzuki","year":"1985","unstructured":"Suzuki, S., et al.: Topological structural analysis of digitized binary images by border following. Comput. Vis. Graph. Image Process. 30(1), 32\u201346 (1985)","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"9_CR28","unstructured":"Roboflow. Roboflow website (2021)"},{"key":"9_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/978-3-319-48881-3_7","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"R Sanchez-Matilla","year":"2016","unstructured":"Sanchez-Matilla, R., Poiesi, F., Cavallaro, A.: Online multi-target tracking with strong and weak detections. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 84\u201399. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48881-3_7"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing. ICIAP 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13324-4_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T19:04:59Z","timestamp":1666465499000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13324-4_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031133237","9783031133244"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13324-4_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lecce","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2021.org\/","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","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","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":"168","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":"55% - 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","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":"4","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)"}}]}}