{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T08:27:43Z","timestamp":1778660863764,"version":"3.51.4"},"reference-count":19,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,11]],"date-time":"2018-12-11T00:00:00Z","timestamp":1544486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010198","name":"Ministerio de Econom\u00eda, Industria y Competitividad, Gobierno de Espa\u00f1a","doi-asserted-by":"publisher","award":["TEC2014-53176-R (HAVideo"],"award-info":[{"award-number":["TEC2014-53176-R (HAVideo"]}],"id":[{"id":"10.13039\/501100010198","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Finding optimal parametrizations for people detectors is a complicated task due to the large number of parameters and the high variability of application scenarios. In this paper, we propose a framework to adapt and improve any detector automatically in multi-camera scenarios where people are observed from various viewpoints. By accurately transferring detector results between camera viewpoints and by self-correlating these transferred results, the best configuration (in this paper, the detection threshold) for each detector-viewpoint pair is identified online without requiring any additional manually-labeled ground truth apart from the offline training of the detection model. Such a configuration consists of establishing the confidence detection threshold present in every people detector, which is a critical parameter affecting detection performance. The experimental results demonstrate that the proposed framework improves the performance of four different state-of-the-art detectors (DPM , ACF, faster R-CNN, and YOLO9000) whose Optimal Fixed Thresholds (OFTs) have been determined and fixed during training time using standard datasets.<\/jats:p>","DOI":"10.3390\/s18124385","type":"journal-article","created":{"date-parts":[[2018,12,12]],"date-time":"2018-12-12T03:27:49Z","timestamp":1544585269000},"page":"4385","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing Multi-Camera People Detection by Online Automatic Parametrization Using Detection Transfer and Self-Correlation Maximization \u2020"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7317-7542","authenticated-orcid":false,"given":"Rafael","family":"Mart\u00edn-Nieto","sequence":"first","affiliation":[{"name":"Video Processing and Understanding Laboratory (VPULab), Universidad Aut\u00f3noma de Madrid, 28049 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1705-3972","authenticated-orcid":false,"given":"\u00c1lvaro","family":"Garc\u00eda-Mart\u00edn","sequence":"additional","affiliation":[{"name":"Video Processing and Understanding Laboratory (VPULab), Universidad Aut\u00f3noma de Madrid, 28049 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2236-1769","authenticated-orcid":false,"given":"Jos\u00e9 M.","family":"Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Video Processing and Understanding Laboratory (VPULab), Universidad Aut\u00f3noma de Madrid, 28049 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4999-2851","authenticated-orcid":false,"given":"Juan C.","family":"SanMiguel","sequence":"additional","affiliation":[{"name":"Video Processing and Understanding Laboratory (VPULab), Universidad Aut\u00f3noma de Madrid, 28049 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1109\/TPAMI.2013.124","article-title":"Scene-Specific Pedestrian Detection for Static Video Surveillance","volume":"36","author":"Wang","year":"2014","journal-title":"IEEE Trans. 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