Species-Agnostic Patterned Animal Re-identification by Aggregating Deep Local Features
Abstract Access to large image volumes through camera traps and crowdsourcing provides novel possibilities for animal monitoring and conservation. It calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. Most existing re-identification metho...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Nepovinnykh, Ekaterina [verfasserIn] Chelak, Ilia [verfasserIn] Eerola, Tuomas [verfasserIn] Immonen, Veikka [verfasserIn] Kälviäinen, Heikki [verfasserIn] Kholiavchenko, Maksim [verfasserIn] Stewart, Charles V. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2024 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of computer vision - Springer US, 1987, 132(2024), 9 vom: 30. Apr., Seite 4003-4018 |
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| Übergeordnetes Werk: |
volume:132 ; year:2024 ; number:9 ; day:30 ; month:04 ; pages:4003-4018 |
| Links: |
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| DOI / URN: |
10.1007/s11263-024-02071-1 |
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| Katalog-ID: |
SPR057107580 |
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| 245 | 1 | 0 | |a Species-Agnostic Patterned Animal Re-identification by Aggregating Deep Local Features |
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| 520 | |a Abstract Access to large image volumes through camera traps and crowdsourcing provides novel possibilities for animal monitoring and conservation. It calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. Most existing re-identification methods rely on either hand-crafted local features or end-to-end learning of fur pattern similarity. The former does not need labeled training data, while the latter, although very data-hungry typically outperforms the former when enough training data is available. We propose a novel re-identification pipeline that combines the strengths of both approaches by utilizing modern learnable local features and feature aggregation. This creates representative pattern feature embeddings that provide high re-identification accuracy while allowing us to apply the method to small datasets by using pre-trained feature descriptors. We report a comprehensive comparison of different modern local features and demonstrate the advantages of the proposed pipeline on two very different species. | ||
| 650 | 4 | |a Computer vision |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image processing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Animal biometrics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Re-identification |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Ringed seals |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Convolutional neural networks |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Chelak, Ilia |e verfasserin |4 aut | |
| 700 | 1 | |a Eerola, Tuomas |e verfasserin |4 aut | |
| 700 | 1 | |a Immonen, Veikka |e verfasserin |4 aut | |
| 700 | 1 | |a Kälviäinen, Heikki |e verfasserin |4 aut | |
| 700 | 1 | |a Kholiavchenko, Maksim |e verfasserin |4 aut | |
| 700 | 1 | |a Stewart, Charles V. |e verfasserin |4 aut | |
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10.1007/s11263-024-02071-1 doi (DE-627)SPR057107580 (SPR)s11263-024-02071-1-e DE-627 ger DE-627 rakwb eng 004 VZ 54.74 bkl Nepovinnykh, Ekaterina verfasserin (orcid)0000-0002-5045-5041 aut Species-Agnostic Patterned Animal Re-identification by Aggregating Deep Local Features 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Access to large image volumes through camera traps and crowdsourcing provides novel possibilities for animal monitoring and conservation. It calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. Most existing re-identification methods rely on either hand-crafted local features or end-to-end learning of fur pattern similarity. The former does not need labeled training data, while the latter, although very data-hungry typically outperforms the former when enough training data is available. We propose a novel re-identification pipeline that combines the strengths of both approaches by utilizing modern learnable local features and feature aggregation. This creates representative pattern feature embeddings that provide high re-identification accuracy while allowing us to apply the method to small datasets by using pre-trained feature descriptors. We report a comprehensive comparison of different modern local features and demonstrate the advantages of the proposed pipeline on two very different species. Computer vision (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Animal biometrics (dpeaa)DE-He213 Re-identification (dpeaa)DE-He213 Ringed seals (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Chelak, Ilia verfasserin aut Eerola, Tuomas verfasserin aut Immonen, Veikka verfasserin aut Kälviäinen, Heikki verfasserin aut Kholiavchenko, Maksim verfasserin aut Stewart, Charles V. verfasserin aut Enthalten in International journal of computer vision Springer US, 1987 132(2024), 9 vom: 30. Apr., Seite 4003-4018 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:132 year:2024 number:9 day:30 month:04 pages:4003-4018 https://dx.doi.org/10.1007/s11263-024-02071-1 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 VZ AR 132 2024 9 30 04 4003-4018 |
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10.1007/s11263-024-02071-1 doi (DE-627)SPR057107580 (SPR)s11263-024-02071-1-e DE-627 ger DE-627 rakwb eng 004 VZ 54.74 bkl Nepovinnykh, Ekaterina verfasserin (orcid)0000-0002-5045-5041 aut Species-Agnostic Patterned Animal Re-identification by Aggregating Deep Local Features 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Access to large image volumes through camera traps and crowdsourcing provides novel possibilities for animal monitoring and conservation. It calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. Most existing re-identification methods rely on either hand-crafted local features or end-to-end learning of fur pattern similarity. The former does not need labeled training data, while the latter, although very data-hungry typically outperforms the former when enough training data is available. We propose a novel re-identification pipeline that combines the strengths of both approaches by utilizing modern learnable local features and feature aggregation. This creates representative pattern feature embeddings that provide high re-identification accuracy while allowing us to apply the method to small datasets by using pre-trained feature descriptors. We report a comprehensive comparison of different modern local features and demonstrate the advantages of the proposed pipeline on two very different species. Computer vision (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Animal biometrics (dpeaa)DE-He213 Re-identification (dpeaa)DE-He213 Ringed seals (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Chelak, Ilia verfasserin aut Eerola, Tuomas verfasserin aut Immonen, Veikka verfasserin aut Kälviäinen, Heikki verfasserin aut Kholiavchenko, Maksim verfasserin aut Stewart, Charles V. verfasserin aut Enthalten in International journal of computer vision Springer US, 1987 132(2024), 9 vom: 30. Apr., Seite 4003-4018 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:132 year:2024 number:9 day:30 month:04 pages:4003-4018 https://dx.doi.org/10.1007/s11263-024-02071-1 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 VZ AR 132 2024 9 30 04 4003-4018 |
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10.1007/s11263-024-02071-1 doi (DE-627)SPR057107580 (SPR)s11263-024-02071-1-e DE-627 ger DE-627 rakwb eng 004 VZ 54.74 bkl Nepovinnykh, Ekaterina verfasserin (orcid)0000-0002-5045-5041 aut Species-Agnostic Patterned Animal Re-identification by Aggregating Deep Local Features 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Access to large image volumes through camera traps and crowdsourcing provides novel possibilities for animal monitoring and conservation. It calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. Most existing re-identification methods rely on either hand-crafted local features or end-to-end learning of fur pattern similarity. The former does not need labeled training data, while the latter, although very data-hungry typically outperforms the former when enough training data is available. We propose a novel re-identification pipeline that combines the strengths of both approaches by utilizing modern learnable local features and feature aggregation. This creates representative pattern feature embeddings that provide high re-identification accuracy while allowing us to apply the method to small datasets by using pre-trained feature descriptors. We report a comprehensive comparison of different modern local features and demonstrate the advantages of the proposed pipeline on two very different species. Computer vision (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Animal biometrics (dpeaa)DE-He213 Re-identification (dpeaa)DE-He213 Ringed seals (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Chelak, Ilia verfasserin aut Eerola, Tuomas verfasserin aut Immonen, Veikka verfasserin aut Kälviäinen, Heikki verfasserin aut Kholiavchenko, Maksim verfasserin aut Stewart, Charles V. verfasserin aut Enthalten in International journal of computer vision Springer US, 1987 132(2024), 9 vom: 30. Apr., Seite 4003-4018 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:132 year:2024 number:9 day:30 month:04 pages:4003-4018 https://dx.doi.org/10.1007/s11263-024-02071-1 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 VZ AR 132 2024 9 30 04 4003-4018 |
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10.1007/s11263-024-02071-1 doi (DE-627)SPR057107580 (SPR)s11263-024-02071-1-e DE-627 ger DE-627 rakwb eng 004 VZ 54.74 bkl Nepovinnykh, Ekaterina verfasserin (orcid)0000-0002-5045-5041 aut Species-Agnostic Patterned Animal Re-identification by Aggregating Deep Local Features 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Access to large image volumes through camera traps and crowdsourcing provides novel possibilities for animal monitoring and conservation. It calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. Most existing re-identification methods rely on either hand-crafted local features or end-to-end learning of fur pattern similarity. The former does not need labeled training data, while the latter, although very data-hungry typically outperforms the former when enough training data is available. We propose a novel re-identification pipeline that combines the strengths of both approaches by utilizing modern learnable local features and feature aggregation. This creates representative pattern feature embeddings that provide high re-identification accuracy while allowing us to apply the method to small datasets by using pre-trained feature descriptors. We report a comprehensive comparison of different modern local features and demonstrate the advantages of the proposed pipeline on two very different species. Computer vision (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Animal biometrics (dpeaa)DE-He213 Re-identification (dpeaa)DE-He213 Ringed seals (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Chelak, Ilia verfasserin aut Eerola, Tuomas verfasserin aut Immonen, Veikka verfasserin aut Kälviäinen, Heikki verfasserin aut Kholiavchenko, Maksim verfasserin aut Stewart, Charles V. verfasserin aut Enthalten in International journal of computer vision Springer US, 1987 132(2024), 9 vom: 30. Apr., Seite 4003-4018 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:132 year:2024 number:9 day:30 month:04 pages:4003-4018 https://dx.doi.org/10.1007/s11263-024-02071-1 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 VZ AR 132 2024 9 30 04 4003-4018 |
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Computer vision Image processing Animal biometrics Re-identification Ringed seals Convolutional neural networks |
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Nepovinnykh, Ekaterina @@aut@@ Chelak, Ilia @@aut@@ Eerola, Tuomas @@aut@@ Immonen, Veikka @@aut@@ Kälviäinen, Heikki @@aut@@ Kholiavchenko, Maksim @@aut@@ Stewart, Charles V. @@aut@@ |
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Nepovinnykh, Ekaterina |
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Nepovinnykh, Ekaterina ddc 004 bkl 54.74 misc Computer vision misc Image processing misc Animal biometrics misc Re-identification misc Ringed seals misc Convolutional neural networks Species-Agnostic Patterned Animal Re-identification by Aggregating Deep Local Features |
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004 VZ 54.74 bkl Species-Agnostic Patterned Animal Re-identification by Aggregating Deep Local Features Computer vision (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Animal biometrics (dpeaa)DE-He213 Re-identification (dpeaa)DE-He213 Ringed seals (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 |
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species-agnostic patterned animal re-identification by aggregating deep local features |
| title_auth |
Species-Agnostic Patterned Animal Re-identification by Aggregating Deep Local Features |
| abstract |
Abstract Access to large image volumes through camera traps and crowdsourcing provides novel possibilities for animal monitoring and conservation. It calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. Most existing re-identification methods rely on either hand-crafted local features or end-to-end learning of fur pattern similarity. The former does not need labeled training data, while the latter, although very data-hungry typically outperforms the former when enough training data is available. We propose a novel re-identification pipeline that combines the strengths of both approaches by utilizing modern learnable local features and feature aggregation. This creates representative pattern feature embeddings that provide high re-identification accuracy while allowing us to apply the method to small datasets by using pre-trained feature descriptors. We report a comprehensive comparison of different modern local features and demonstrate the advantages of the proposed pipeline on two very different species. © The Author(s) 2024 |
| abstractGer |
Abstract Access to large image volumes through camera traps and crowdsourcing provides novel possibilities for animal monitoring and conservation. It calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. Most existing re-identification methods rely on either hand-crafted local features or end-to-end learning of fur pattern similarity. The former does not need labeled training data, while the latter, although very data-hungry typically outperforms the former when enough training data is available. We propose a novel re-identification pipeline that combines the strengths of both approaches by utilizing modern learnable local features and feature aggregation. This creates representative pattern feature embeddings that provide high re-identification accuracy while allowing us to apply the method to small datasets by using pre-trained feature descriptors. We report a comprehensive comparison of different modern local features and demonstrate the advantages of the proposed pipeline on two very different species. © The Author(s) 2024 |
| abstract_unstemmed |
Abstract Access to large image volumes through camera traps and crowdsourcing provides novel possibilities for animal monitoring and conservation. It calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. Most existing re-identification methods rely on either hand-crafted local features or end-to-end learning of fur pattern similarity. The former does not need labeled training data, while the latter, although very data-hungry typically outperforms the former when enough training data is available. We propose a novel re-identification pipeline that combines the strengths of both approaches by utilizing modern learnable local features and feature aggregation. This creates representative pattern feature embeddings that provide high re-identification accuracy while allowing us to apply the method to small datasets by using pre-trained feature descriptors. We report a comprehensive comparison of different modern local features and demonstrate the advantages of the proposed pipeline on two very different species. © The Author(s) 2024 |
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| title_short |
Species-Agnostic Patterned Animal Re-identification by Aggregating Deep Local Features |
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https://dx.doi.org/10.1007/s11263-024-02071-1 |
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| author2 |
Chelak, Ilia Eerola, Tuomas Immonen, Veikka Kälviäinen, Heikki Kholiavchenko, Maksim Stewart, Charles V. |
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Chelak, Ilia Eerola, Tuomas Immonen, Veikka Kälviäinen, Heikki Kholiavchenko, Maksim Stewart, Charles V. |
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2024-08-28T05:54:12.106Z |
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|
| score |
7.401991 |