Neutrosophic Similarity Score Based Weighted Histogram for Robust Mean-Shift Tracking
Visual object tracking is a critical task in computer vision. Challenging things always exist when an object needs to be tracked. For instance, background clutter is one of the most challenging problems. The mean-shift tracker is quite popular because of its efficiency and performance in a range of...
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MDPI AG
2017-10-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/8/4/122 |
| _version_ | 1828760664690130944 |
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| author | Keli Hu En Fan Jun Ye Changxing Fan Shigen Shen Yuzhang Gu |
| author_facet | Keli Hu En Fan Jun Ye Changxing Fan Shigen Shen Yuzhang Gu |
| author_sort | Keli Hu |
| collection | DOAJ |
| description | Visual object tracking is a critical task in computer vision. Challenging things always exist when an object needs to be tracked. For instance, background clutter is one of the most challenging problems. The mean-shift tracker is quite popular because of its efficiency and performance in a range of conditions. However, the challenge of background clutter also disturbs its performance. In this article, we propose a novel weighted histogram based on neutrosophic similarity score to help the mean-shift tracker discriminate the target from the background. Neutrosophic set (NS) is a new branch of philosophy for dealing with incomplete, indeterminate, and inconsistent information. In this paper, we utilize the single valued neutrosophic set (SVNS), which is a subclass of NS to improve the mean-shift tracker. First, two kinds of criteria are considered as the object feature similarity and the background feature similarity, and each bin of the weight histogram is represented in the SVNS domain via three membership functions T(Truth), I(indeterminacy), and F(Falsity). Second, the neutrosophic similarity score function is introduced to fuse those two criteria and to build the final weight histogram. Finally, a novel neutrosophic weighted mean-shift tracker is proposed. The proposed tracker is compared with several mean-shift based trackers on a dataset of 61 public sequences. The results revealed that our method outperforms other trackers, especially when confronting background clutter. |
| first_indexed | 2024-12-11T01:17:20Z |
| format | Article |
| id | doaj.art-1523867d9a344cfb957e312d4345450a |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-12-11T01:17:20Z |
| publishDate | 2017-10-01 |
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| spelling | doaj.art-1523867d9a344cfb957e312d4345450a2022-12-22T01:25:49ZengMDPI AGInformation2078-24892017-10-018412210.3390/info8040122info8040122Neutrosophic Similarity Score Based Weighted Histogram for Robust Mean-Shift TrackingKeli Hu0En Fan1Jun Ye2Changxing Fan3Shigen Shen4Yuzhang Gu5Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, ChinaDepartment of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, ChinaDepartment of Electrical and Information Engineering, Shaoxing University, Shaoxing 312000, ChinaDepartment of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, ChinaDepartment of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, ChinaKey Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem andInformation Technology, Chinese Academy of Sciences, Shanghai 200050, ChinaVisual object tracking is a critical task in computer vision. Challenging things always exist when an object needs to be tracked. For instance, background clutter is one of the most challenging problems. The mean-shift tracker is quite popular because of its efficiency and performance in a range of conditions. However, the challenge of background clutter also disturbs its performance. In this article, we propose a novel weighted histogram based on neutrosophic similarity score to help the mean-shift tracker discriminate the target from the background. Neutrosophic set (NS) is a new branch of philosophy for dealing with incomplete, indeterminate, and inconsistent information. In this paper, we utilize the single valued neutrosophic set (SVNS), which is a subclass of NS to improve the mean-shift tracker. First, two kinds of criteria are considered as the object feature similarity and the background feature similarity, and each bin of the weight histogram is represented in the SVNS domain via three membership functions T(Truth), I(indeterminacy), and F(Falsity). Second, the neutrosophic similarity score function is introduced to fuse those two criteria and to build the final weight histogram. Finally, a novel neutrosophic weighted mean-shift tracker is proposed. The proposed tracker is compared with several mean-shift based trackers on a dataset of 61 public sequences. The results revealed that our method outperforms other trackers, especially when confronting background clutter.https://www.mdpi.com/2078-2489/8/4/122trackingmean-shiftneutrosophic setsingle valued neutrosophic setneutrosophic similarity score |
| spellingShingle | Keli Hu En Fan Jun Ye Changxing Fan Shigen Shen Yuzhang Gu Neutrosophic Similarity Score Based Weighted Histogram for Robust Mean-Shift Tracking Information tracking mean-shift neutrosophic set single valued neutrosophic set neutrosophic similarity score |
| title | Neutrosophic Similarity Score Based Weighted Histogram for Robust Mean-Shift Tracking |
| title_full | Neutrosophic Similarity Score Based Weighted Histogram for Robust Mean-Shift Tracking |
| title_fullStr | Neutrosophic Similarity Score Based Weighted Histogram for Robust Mean-Shift Tracking |
| title_full_unstemmed | Neutrosophic Similarity Score Based Weighted Histogram for Robust Mean-Shift Tracking |
| title_short | Neutrosophic Similarity Score Based Weighted Histogram for Robust Mean-Shift Tracking |
| title_sort | neutrosophic similarity score based weighted histogram for robust mean shift tracking |
| topic | tracking mean-shift neutrosophic set single valued neutrosophic set neutrosophic similarity score |
| url | https://www.mdpi.com/2078-2489/8/4/122 |
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