Multimodal Failure Matching Point Based Motion Object Saliency Detection for Unconstrained Videos
Inspired by classical feature descriptors in motion matching, this paper proposes a multimodal failure matching point collection method, which is defined as FMP. FMP is, in fact, a collection of unstable features with a low matching degree in the conventional matching task. Based on FMP, a novel mod...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2022.2110695 |
| _version_ | 1797641069756678144 |
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| author | Jiang Qian Jingkang Wei Hui Chen Gongping Chen |
| author_facet | Jiang Qian Jingkang Wei Hui Chen Gongping Chen |
| author_sort | Jiang Qian |
| collection | DOAJ |
| description | Inspired by classical feature descriptors in motion matching, this paper proposes a multimodal failure matching point collection method, which is defined as FMP. FMP is, in fact, a collection of unstable features with a low matching degree in the conventional matching task. Based on FMP, a novel model for the saliency detection of motion object is developed. Models are evaluated on the DAVIS and SegTrackv2 datasets and compared with recently advanced object detection algorithms. The comparison results demonstrate the availability and effectiveness of FMP in the detection of motion object saliency. |
| first_indexed | 2024-03-11T13:40:13Z |
| format | Article |
| id | doaj.art-491002d9294e492ea707c457ec74790a |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-11T13:40:13Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-491002d9294e492ea707c457ec74790a2023-11-02T13:36:38ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452022-12-0136110.1080/08839514.2022.21106952110695Multimodal Failure Matching Point Based Motion Object Saliency Detection for Unconstrained VideosJiang Qian0Jingkang Wei1Hui Chen2Gongping Chen3Anhui Business CollegeTianfu International Airport Branch CompanyAnhui Business CollegeCollege of Artificial Intelligence, Nankai UniversityInspired by classical feature descriptors in motion matching, this paper proposes a multimodal failure matching point collection method, which is defined as FMP. FMP is, in fact, a collection of unstable features with a low matching degree in the conventional matching task. Based on FMP, a novel model for the saliency detection of motion object is developed. Models are evaluated on the DAVIS and SegTrackv2 datasets and compared with recently advanced object detection algorithms. The comparison results demonstrate the availability and effectiveness of FMP in the detection of motion object saliency.http://dx.doi.org/10.1080/08839514.2022.2110695 |
| spellingShingle | Jiang Qian Jingkang Wei Hui Chen Gongping Chen Multimodal Failure Matching Point Based Motion Object Saliency Detection for Unconstrained Videos Applied Artificial Intelligence |
| title | Multimodal Failure Matching Point Based Motion Object Saliency Detection for Unconstrained Videos |
| title_full | Multimodal Failure Matching Point Based Motion Object Saliency Detection for Unconstrained Videos |
| title_fullStr | Multimodal Failure Matching Point Based Motion Object Saliency Detection for Unconstrained Videos |
| title_full_unstemmed | Multimodal Failure Matching Point Based Motion Object Saliency Detection for Unconstrained Videos |
| title_short | Multimodal Failure Matching Point Based Motion Object Saliency Detection for Unconstrained Videos |
| title_sort | multimodal failure matching point based motion object saliency detection for unconstrained videos |
| url | http://dx.doi.org/10.1080/08839514.2022.2110695 |
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