{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T09:33:46Z","timestamp":1761989626715,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171224","41771157"],"award-info":[{"award-number":["42171224","41771157"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China","award":["2018YFB0505400"],"award-info":[{"award-number":["2018YFB0505400"]}]},{"name":"the Great Wall Scholars Program","award":["CIT&TCD20190328"],"award-info":[{"award-number":["CIT&TCD20190328"]}]},{"name":"Key Research Projects of National Statistical Science of China","award":["2021LZ23"],"award-info":[{"award-number":["2021LZ23"]}]},{"name":"Young Yanjing Scholar Project of Capital Normal University ,and Academy for Multidiscipli-nary Studies","award":["Capital Normal University"],"award-info":[{"award-number":["Capital Normal University"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Previous research on moving object detection in traffic surveillance video has mostly adopted a single threshold to eliminate the noise caused by external environmental interference, resulting in low accuracy and low efficiency of moving object detection. Therefore, we propose a moving object detection method that considers the difference of image spatial threshold, i.e., a moving object detection method using adaptive threshold (MOD-AT for short). In particular, based on the homograph method, we first establish the mapping relationship between the geometric-imaging characteristics of moving objects in the image space and the minimum circumscribed rectangle (BLOB) of moving objects in the geographic space to calculate the projected size of moving objects in the image space, by which we can set an adaptive threshold for each moving object to precisely remove the noise interference during moving object detection. Further, we propose a moving object detection algorithm called GMM_BLOB (GMM denotes Gaussian mixture model) to achieve high-precision detection and noise removal of moving objects. The case-study results show the following: (1) Compared with the existing object detection algorithm, the median error (MD) of the MOD-AT algorithm is reduced by 1.2\u201311.05%, and the mean error (MN) is reduced by 1.5\u201315.5%, indicating that the accuracy of the MOD-AT algorithm is higher in single-frame detection; (2) in terms of overall accuracy, the performance and time efficiency of the MOD-AT algorithm is improved by 7.9\u201324.3%, reflecting the higher efficiency of the MOD-AT algorithm; (3) the average accuracy (MP) of the MOD-AT algorithm is improved by 17.13\u201344.4%, the average recall (MR) by 7.98\u201324.38%, and the average F1-score (MF) by 10.13\u201333.97%; in general, the MOD-AT algorithm is more accurate, efficient, and robust.<\/jats:p>","DOI":"10.3390\/ijgi10110742","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:21:34Z","timestamp":1635805294000},"page":"742","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold"],"prefix":"10.3390","volume":"10","author":[{"given":"Xiaoyue","family":"Luo","sequence":"first","affiliation":[{"name":"College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"}]},{"given":"Yanhui","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"}]},{"given":"Benhe","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"}]},{"given":"Zhanxing","family":"Li","sequence":"additional","affiliation":[{"name":"College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,1]]},"reference":[{"key":"ref_1","first-page":"119","article-title":"Video Specialization Method and Its Application","volume":"37","author":"Yong","year":"2014","journal-title":"J. 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