{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T05:02:45Z","timestamp":1744434165859,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031442223"},{"type":"electronic","value":"9783031442230"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44223-0_2","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T05:01:41Z","timestamp":1695272501000},"page":"13-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Air-to-Ground Active Object Tracking via\u00a0Reinforcement Learning"],"prefix":"10.1007","author":[{"given":"Xin","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiya","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1868-0123","authenticated-orcid":false,"given":"Jie","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaochuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoguang","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huadong","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","DOI":"10.4324\/9781315806730","volume-title":"Ordinal Methods for Behavioral Data Analysis","author":"N Cliff","year":"2014","unstructured":"Cliff, N.: Ordinal Methods for Behavioral Data Analysis. Psychology Press, London (2014)"},{"key":"2_CR2","first-page":"1","volume":"60","author":"Y Cui","year":"2021","unstructured":"Cui, Y., Hou, B., Wu, Q., Ren, B., Wang, S., Jiao, L.: Remote sensing object tracking with deep reinforcement learning under occlusion. IEEE Trans. Geoscience Remote Sens. 60, 1\u201313 (2021)","journal-title":"IEEE Trans. Geoscience Remote Sens."},{"key":"2_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2021.103799","volume":"142","author":"A Devo","year":"2021","unstructured":"Devo, A., Dionigi, A., Costante, G.: Enhancing continuous control of mobile robots for end-to-end visual active tracking. Robot. Auton. Syst. 142, 103799 (2021)","journal-title":"Robot. Auton. Syst."},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Jeong, H., Hassani, H., Morari, M., Lee, D.D., Pappas, G.J.: Deep reinforcement learning for active target tracking. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 1825\u20131831. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9561258"},{"key":"2_CR5","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1007\/s11554-021-01077-z","volume":"18","author":"C Kyrkou","year":"2021","unstructured":"Kyrkou, C.: C$$^3$$Net: end-to-end deep learning for efficient real-time visual active camera control. J. Real-Time Image Proc. 18, 1421\u20131433 (2021)","journal-title":"J. Real-Time Image Proc."},{"key":"2_CR6","doi-asserted-by":"publisher","first-page":"85265","DOI":"10.1109\/ACCESS.2021.3082947","volume":"9","author":"Y Luo","year":"2021","unstructured":"Luo, Y., et al.: Calibration-free monocular vision-based robot manipulations with occlusion awareness. IEEE Access 9, 85265\u201385276 (2021)","journal-title":"IEEE Access"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Ma, X., Wang, Y., Yang, S., Niu, W., Ma, W.: Trajectory tracking of an underwater glider in current based on deep reinforcement learning. In: OCEANS 2021, San Diego-Porto, pp. 1\u20137. IEEE (2021)","DOI":"10.23919\/OCEANS44145.2021.9705882"},{"key":"2_CR8","unstructured":"Romano, J., Kromrey, J.D., Coraggio, J., Skowronek, J.: Appropriate statistics for ordinal level data: should we really be using t-test and Cohen\u2019s d for evaluating group differences on the NSSE and other surveys. In: Annual Meeting of the Florida Association of Institutional Research, vol. 177, p. 34 (2006)"},{"issue":"1","key":"2_CR9","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1111\/j.1541-0420.2005.00389.x","volume":"62","author":"B Rosner","year":"2006","unstructured":"Rosner, B., Glynn, R.J., Lee, M.L.T.: The Wilcoxon signed rank test for paired comparisons of clustered data. Biometrics 62(1), 185\u2013192 (2006)","journal-title":"Biometrics"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Ross, D.L., Lim, J.L., et al.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1r3), 125r141 (2008)","DOI":"10.1007\/s11263-007-0075-7"},{"key":"2_CR11","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)"},{"issue":"6","key":"2_CR12","doi-asserted-by":"publisher","first-page":"3697","DOI":"10.1109\/TCSVT.2021.3107153","volume":"32","author":"M Xi","year":"2021","unstructured":"Xi, M., Zhou, Y., Chen, Z., Zhou, W., Li, H.: Anti-distractor active object tracking in 3D environments. IEEE Trans. Circuits Syst. Video Technol. 32(6), 3697\u20133707 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Yang, J., Tang, Z., Pei, Z., Song, X.: A novel motion-intelligence-based control algorithm for object tracking by controlling pan-tilt automatically. Math. Probl. Eng. 2019 (2019)","DOI":"10.1155\/2019\/9602460"},{"key":"2_CR14","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.neunet.2022.01.010","volume":"148","author":"B Yao","year":"2022","unstructured":"Yao, B.: GARAT: generative adversarial learning for robust and accurate tracking. Neural Netw. 148, 206\u2013218 (2022)","journal-title":"Neural Netw."},{"issue":"6","key":"2_CR15","doi-asserted-by":"publisher","first-page":"2239","DOI":"10.1109\/TNNLS.2018.2801826","volume":"29","author":"S Yun","year":"2018","unstructured":"Yun, S., Choi, J., Yoo, Y., Yun, K., Choi, J.Y.: Action-driven visual object tracking with deep reinforcement learning. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2239\u20132252 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, H., He, P., Zhang, M., Chen, D., Neretin, E., Li, B.: UAV target tracking method based on deep reinforcement learning. In: 2022 International Conference on Cyber-Physical Social Intelligence (ICCSI), pp. 274\u2013277. IEEE (2022)","DOI":"10.1109\/ICCSI55536.2022.9970588"},{"issue":"22","key":"2_CR17","doi-asserted-by":"publisher","first-page":"10595","DOI":"10.3390\/app112210595","volume":"11","author":"W Zhao","year":"2021","unstructured":"Zhao, W., Meng, Z., Wang, K., Zhang, J., Lu, S.: Hierarchical active tracking control for UAVs via deep reinforcement learning. Appl. Sci. 11(22), 10595 (2021)","journal-title":"Appl. Sci."},{"key":"2_CR18","unstructured":"Zhong, F., Sun, P., Luo, W., Yan, T., Wang, Y.: Towards distraction-robust active visual tracking. In: International Conference on Machine Learning, pp. 12782\u201312792. PMLR (2021)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44223-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T06:11:02Z","timestamp":1695276662000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44223-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442223","9783031442230"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44223-0_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"type of other papers accepted  : 9 Abstract","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}