Robust Detection of Abandoned Object for Smart Video Surveillance in Illumination Changes
Most existing abandoned object detection algorithms use foreground information generated from background models. Detection using the background subtraction technique performs well under normal circumstances. However, it has a significant problem where the foreground information is gradually absorbed...
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| Format: | Article |
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MDPI AG
2019-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/23/5114 |
| _version_ | 1811300451125035008 |
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| author | Hyeseung Park Seungchul Park Youngbok Joo |
| author_facet | Hyeseung Park Seungchul Park Youngbok Joo |
| author_sort | Hyeseung Park |
| collection | DOAJ |
| description | Most existing abandoned object detection algorithms use foreground information generated from background models. Detection using the background subtraction technique performs well under normal circumstances. However, it has a significant problem where the foreground information is gradually absorbed into the background as time passes and disappears, making it very vulnerable to sudden illumination changes that increase the false alarm rate. This paper presents an algorithm for detecting abandoned objects using a dual background model, which is robust even in illumination changes as well as other complex circumstances like occlusion, long-term abandonment, and owner re-attendance. The proposed algorithm can adapt quickly to various illumination changes. And also, it can precisely track the target objects to determine whether it is abandoned regardless of the existence of foreground information and the effect from the illumination changes, thanks to the largest-contour-based presence authentication mechanism proposed in this paper. For performance evaluation, we trialed the algorithm with the PETS2006, ABODA datasets as well as our dataset, especially to demonstrate its robustness in various illumination changes. |
| first_indexed | 2024-04-13T06:52:21Z |
| format | Article |
| id | doaj.art-812bd44f9e3b4c898a834989ee194f1e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T06:52:21Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-812bd44f9e3b4c898a834989ee194f1e2022-12-22T02:57:22ZengMDPI AGSensors1424-82202019-11-011923511410.3390/s19235114s19235114Robust Detection of Abandoned Object for Smart Video Surveillance in Illumination ChangesHyeseung Park0Seungchul Park1Youngbok Joo2School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeol-ro, Byeongcheon-myeon, Dongnam-gu, Cheonan-si, Chungcheongnam-do, KoreaSchool of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeol-ro, Byeongcheon-myeon, Dongnam-gu, Cheonan-si, Chungcheongnam-do, KoreaSchool of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeol-ro, Byeongcheon-myeon, Dongnam-gu, Cheonan-si, Chungcheongnam-do, KoreaMost existing abandoned object detection algorithms use foreground information generated from background models. Detection using the background subtraction technique performs well under normal circumstances. However, it has a significant problem where the foreground information is gradually absorbed into the background as time passes and disappears, making it very vulnerable to sudden illumination changes that increase the false alarm rate. This paper presents an algorithm for detecting abandoned objects using a dual background model, which is robust even in illumination changes as well as other complex circumstances like occlusion, long-term abandonment, and owner re-attendance. The proposed algorithm can adapt quickly to various illumination changes. And also, it can precisely track the target objects to determine whether it is abandoned regardless of the existence of foreground information and the effect from the illumination changes, thanks to the largest-contour-based presence authentication mechanism proposed in this paper. For performance evaluation, we trialed the algorithm with the PETS2006, ABODA datasets as well as our dataset, especially to demonstrate its robustness in various illumination changes.https://www.mdpi.com/1424-8220/19/23/5114abandoned object detectionpets2006abodaocclusionillumination changesmart video surveillanceunattended object detection |
| spellingShingle | Hyeseung Park Seungchul Park Youngbok Joo Robust Detection of Abandoned Object for Smart Video Surveillance in Illumination Changes Sensors abandoned object detection pets2006 aboda occlusion illumination change smart video surveillance unattended object detection |
| title | Robust Detection of Abandoned Object for Smart Video Surveillance in Illumination Changes |
| title_full | Robust Detection of Abandoned Object for Smart Video Surveillance in Illumination Changes |
| title_fullStr | Robust Detection of Abandoned Object for Smart Video Surveillance in Illumination Changes |
| title_full_unstemmed | Robust Detection of Abandoned Object for Smart Video Surveillance in Illumination Changes |
| title_short | Robust Detection of Abandoned Object for Smart Video Surveillance in Illumination Changes |
| title_sort | robust detection of abandoned object for smart video surveillance in illumination changes |
| topic | abandoned object detection pets2006 aboda occlusion illumination change smart video surveillance unattended object detection |
| url | https://www.mdpi.com/1424-8220/19/23/5114 |
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