The moving target tracking and segmentation method based on space-time fusion Article Swipe
Jie Wang
,
Shibin Xuan
,
Hao Zhang
,
Xuyang Qin
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s11042-022-13703-4
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s11042-022-13703-4
At present, the target tracking method based on the correlation operation mainly uses deep learning to extract spatial information from video frames and then performs correlations on this basis. However, it does not extract the motion features of tracking targets on the time axis, and thus tracked targets can be easily lost when occlusion occurs. To this end, a spatiotemporal motion target tracking model incorporating Kalman filtering is proposed with the aim of alleviating the problem of occlusion in the tracking process. In combination with the segmentation model, a suitable model is selected by scores to predict or detect the current state of the target. We use an elliptic fitting strategy to evaluate the bounding boxes online. Experiments demonstrate that our approach performs well and is stable in the face of multiple challenges (such as occlusion) on the VOT2016 and VOT2018 datasets with guaranteed real-time algorithm performance.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11042-022-13703-4
- https://link.springer.com/content/pdf/10.1007/s11042-022-13703-4.pdf
- OA Status
- hybrid
- Cited By
- 1
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4296961727
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4296961727Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s11042-022-13703-4Digital Object Identifier
- Title
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The moving target tracking and segmentation method based on space-time fusionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-09-21Full publication date if available
- Authors
-
Jie Wang, Shibin Xuan, Hao Zhang, Xuyang QinList of authors in order
- Landing page
-
https://doi.org/10.1007/s11042-022-13703-4Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s11042-022-13703-4.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s11042-022-13703-4.pdfDirect OA link when available
- Concepts
-
Computer science, Artificial intelligence, Computer vision, Segmentation, Tracking (education), Bounding overwatch, Minimum bounding box, Kalman filter, Motion (physics), Process (computing), Pattern recognition (psychology), Image (mathematics), Psychology, Operating system, PedagogyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.We | 106 |
| abstract_inverted_index.an | 108 |
| abstract_inverted_index.as | 135 |
| abstract_inverted_index.be | 50 |
| abstract_inverted_index.by | 94 |
| abstract_inverted_index.in | 79, 128 |
| abstract_inverted_index.is | 68, 92, 126 |
| abstract_inverted_index.it | 31 |
| abstract_inverted_index.of | 38, 73, 77, 103, 131 |
| abstract_inverted_index.on | 8, 27, 41, 137 |
| abstract_inverted_index.or | 98 |
| abstract_inverted_index.to | 16, 96, 112 |
| abstract_inverted_index.aim | 72 |
| abstract_inverted_index.and | 23, 45, 125, 140 |
| abstract_inverted_index.can | 49 |
| abstract_inverted_index.not | 33 |
| abstract_inverted_index.our | 121 |
| abstract_inverted_index.the | 3, 9, 35, 42, 71, 75, 80, 86, 100, 104, 114, 129, 138 |
| abstract_inverted_index.use | 107 |
| abstract_inverted_index.deep | 14 |
| abstract_inverted_index.does | 32 |
| abstract_inverted_index.end, | 58 |
| abstract_inverted_index.face | 130 |
| abstract_inverted_index.from | 20 |
| abstract_inverted_index.lost | 52 |
| abstract_inverted_index.that | 120 |
| abstract_inverted_index.then | 24 |
| abstract_inverted_index.this | 28, 57 |
| abstract_inverted_index.thus | 46 |
| abstract_inverted_index.time | 43 |
| abstract_inverted_index.uses | 13 |
| abstract_inverted_index.well | 124 |
| abstract_inverted_index.when | 53 |
| abstract_inverted_index.with | 70, 85, 143 |
| abstract_inverted_index.(such | 134 |
| abstract_inverted_index.axis, | 44 |
| abstract_inverted_index.based | 7 |
| abstract_inverted_index.boxes | 116 |
| abstract_inverted_index.model | 64, 91 |
| abstract_inverted_index.state | 102 |
| abstract_inverted_index.video | 21 |
| abstract_inverted_index.Kalman | 66 |
| abstract_inverted_index.basis. | 29 |
| abstract_inverted_index.detect | 99 |
| abstract_inverted_index.easily | 51 |
| abstract_inverted_index.frames | 22 |
| abstract_inverted_index.mainly | 12 |
| abstract_inverted_index.method | 6 |
| abstract_inverted_index.model, | 88 |
| abstract_inverted_index.motion | 36, 61 |
| abstract_inverted_index.scores | 95 |
| abstract_inverted_index.stable | 127 |
| abstract_inverted_index.target | 4, 62 |
| abstract_inverted_index.VOT2016 | 139 |
| abstract_inverted_index.VOT2018 | 141 |
| abstract_inverted_index.current | 101 |
| abstract_inverted_index.extract | 17, 34 |
| abstract_inverted_index.fitting | 110 |
| abstract_inverted_index.occurs. | 55 |
| abstract_inverted_index.online. | 117 |
| abstract_inverted_index.predict | 97 |
| abstract_inverted_index.problem | 76 |
| abstract_inverted_index.spatial | 18 |
| abstract_inverted_index.target. | 105 |
| abstract_inverted_index.targets | 40, 48 |
| abstract_inverted_index.tracked | 47 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 30 |
| abstract_inverted_index.approach | 122 |
| abstract_inverted_index.bounding | 115 |
| abstract_inverted_index.datasets | 142 |
| abstract_inverted_index.elliptic | 109 |
| abstract_inverted_index.evaluate | 113 |
| abstract_inverted_index.features | 37 |
| abstract_inverted_index.learning | 15 |
| abstract_inverted_index.multiple | 132 |
| abstract_inverted_index.performs | 25, 123 |
| abstract_inverted_index.present, | 2 |
| abstract_inverted_index.process. | 82 |
| abstract_inverted_index.proposed | 69 |
| abstract_inverted_index.selected | 93 |
| abstract_inverted_index.strategy | 111 |
| abstract_inverted_index.suitable | 90 |
| abstract_inverted_index.tracking | 5, 39, 63, 81 |
| abstract_inverted_index.algorithm | 146 |
| abstract_inverted_index.filtering | 67 |
| abstract_inverted_index.occlusion | 54, 78 |
| abstract_inverted_index.operation | 11 |
| abstract_inverted_index.real-time | 145 |
| abstract_inverted_index.challenges | 133 |
| abstract_inverted_index.guaranteed | 144 |
| abstract_inverted_index.occlusion) | 136 |
| abstract_inverted_index.Experiments | 118 |
| abstract_inverted_index.alleviating | 74 |
| abstract_inverted_index.combination | 84 |
| abstract_inverted_index.correlation | 10 |
| abstract_inverted_index.demonstrate | 119 |
| abstract_inverted_index.information | 19 |
| abstract_inverted_index.correlations | 26 |
| abstract_inverted_index.performance. | 147 |
| abstract_inverted_index.segmentation | 87 |
| abstract_inverted_index.incorporating | 65 |
| abstract_inverted_index.spatiotemporal | 60 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.40672922 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |