{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T23:48:00Z","timestamp":1771458480663,"version":"3.50.1"},"reference-count":20,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2023-00218176"],"award-info":[{"award-number":["RS-2023-00218176"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["P0012724"],"award-info":[{"award-number":["P0012724"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology","doi-asserted-by":"publisher","award":["RS-2023-00218176"],"award-info":[{"award-number":["RS-2023-00218176"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology","doi-asserted-by":"publisher","award":["P0012724"],"award-info":[{"award-number":["P0012724"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Soonchunhyang University","award":["RS-2023-00218176"],"award-info":[{"award-number":["RS-2023-00218176"]}]},{"name":"Soonchunhyang University","award":["P0012724"],"award-info":[{"award-number":["P0012724"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The increasing demand for artificially intelligent smartphone cradles has prompted the need for real-time moving object detection. Real-time moving object tracking requires the development of algorithms for instant tracking analysis without delays. In particular, developing a system for smartphones should consider different operating systems and software development environments. Issues in current real-time moving object tracking systems arise when small and large objects coexist, causing the algorithm to prioritize larger objects or struggle with consistent tracking across varying scales. Fast object motion further complicates accurate tracking and leads to potential errors and misidentification. To address these issues, we propose a deep learning-based real-time moving object tracking system which provides an accuracy priority mode and a speed priority mode. The accuracy priority mode achieves a balance between the high accuracy and speed required in the smartphone environment. The speed priority mode optimizes the speed of inference to track fast-moving objects. The accuracy priority mode incorporates CSPNet with ResNet to maintain high accuracy, whereas the speed priority mode simplifies the complexity of the convolutional layer while maintaining accuracy. In our experiments, we evaluated both modes in terms of accuracy and speed.<\/jats:p>","DOI":"10.3390\/s24041265","type":"journal-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T08:02:17Z","timestamp":1708070537000},"page":"1265","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Real-Time Moving Object Tracking on Smartphone Using Cradle Head Servo Motor"],"prefix":"10.3390","volume":"24","author":[{"given":"Neunggyu","family":"Han","sequence":"first","affiliation":[{"name":"Department of ICT Convergence, Soonchunhyang University, Asan 31538, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sun Joo","family":"Ryu","sequence":"additional","affiliation":[{"name":"Department of Enterprise School, Soonchunhyang University, Asan 31538, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3318-9394","authenticated-orcid":false,"given":"Yunyoung","family":"Nam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1109\/TSMC.2017.2783947","article-title":"Real-Time Pose Imitation by Mid-Size Humanoid Robot with Servo-Cradle-Head RGB-D Vision System","volume":"49","author":"Hwang","year":"2019","journal-title":"IEEE Trans. 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