Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold

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 detect...

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Autores principales:	Xiaoyue Luo, Yanhui Wang, Benhe Cai, Zhanxing Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	video GIS moving object detection threshold differentiation MOD-AT BLOB mapping relationship Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/bdcb017c482347e488b119ed0cb8e7db
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id	oai:doaj.org-article:bdcb017c482347e488b119ed0cb8e7db
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spelling	oai:doaj.org-article:bdcb017c482347e488b119ed0cb8e7db2021-11-25T17:52:53ZMoving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold10.3390/ijgi101107422220-9964https://doaj.org/article/bdcb017c482347e488b119ed0cb8e7db2021-11-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/742https://doaj.org/toc/2220-9964Previous 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 1.2–11.05%, and the mean error (MN) is reduced by 1.5–15.5%, 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 7.9–24.3%, reflecting the higher efficiency of the MOD-AT algorithm; (3) the average accuracy (MP) of the MOD-AT algorithm is improved by 17.13–44.4%, the average recall (MR) by 7.98–24.38%, and the average F1-score (MF) by 10.13–33.97%; in general, the MOD-AT algorithm is more accurate, efficient, and robust.Xiaoyue LuoYanhui WangBenhe CaiZhanxing LiMDPI AGarticlevideo GISmoving object detectionthreshold differentiationMOD-ATBLOBmapping relationshipGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 742, p 742 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	video GIS moving object detection threshold differentiation MOD-AT BLOB mapping relationship Geography (General) G1-922
spellingShingle	video GIS moving object detection threshold differentiation MOD-AT BLOB mapping relationship Geography (General) G1-922 Xiaoyue Luo Yanhui Wang Benhe Cai Zhanxing Li Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold
description	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 1.2–11.05%, and the mean error (MN) is reduced by 1.5–15.5%, 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 7.9–24.3%, reflecting the higher efficiency of the MOD-AT algorithm; (3) the average accuracy (MP) of the MOD-AT algorithm is improved by 17.13–44.4%, the average recall (MR) by 7.98–24.38%, and the average F1-score (MF) by 10.13–33.97%; in general, the MOD-AT algorithm is more accurate, efficient, and robust.
format	article
author	Xiaoyue Luo Yanhui Wang Benhe Cai Zhanxing Li
author_facet	Xiaoyue Luo Yanhui Wang Benhe Cai Zhanxing Li
author_sort	Xiaoyue Luo
title	Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold
title_short	Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold
title_full	Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold
title_fullStr	Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold
title_full_unstemmed	Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold
title_sort	moving object detection in traffic surveillance video: new mod-at method based on adaptive threshold
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bdcb017c482347e488b119ed0cb8e7db
work_keys_str_mv	AT xiaoyueluo movingobjectdetectionintrafficsurveillancevideonewmodatmethodbasedonadaptivethreshold AT yanhuiwang movingobjectdetectionintrafficsurveillancevideonewmodatmethodbasedonadaptivethreshold AT benhecai movingobjectdetectionintrafficsurveillancevideonewmodatmethodbasedonadaptivethreshold AT zhanxingli movingobjectdetectionintrafficsurveillancevideonewmodatmethodbasedonadaptivethreshold
_version_	1718411884686737408

Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold

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