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A collection of challenging motion segmentation benchmark datasets

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http://hdl.handle.net/10256/13152

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Title:: A collection of challenging motion segmentation benchmark datasets
Subject:: Imatges -- Processament; Image processing; Imatges -- Segmentació; Imaging segmentation; Visió per ordinador; Computer vision
Description:: An in-depth analysis of computer vision methodologies is greatly dependent on the benchmarks they are tested upon. Any dataset is as good as the diversity of the true nature of the problem enclosed in it. Motion segmentation is a preprocessing step in computer vision whose publicly available datasets have certain limitations. Some databases are not up-to-date with modern requirements of frame length and number of motions, and others do not have ample ground truth in them. In this paper, we present a collection of diverse multifaceted motion segmentation benchmarks containing trajectory- and region-based ground truth. These datasets enclose real-life long and short sequences, with increased number of motions and frames per sequence, and also real distortions with missing data. The ground truth is provided on all the frames of all the sequences. A comprehensive benchmark evaluation of the state-of-the-art motion segmentation algorithms is provided to establish the difficulty of the problem and to also contribute a starting point. All the resources of the datasets have been made publicly available at http://dixie.udg.edu/udgms/; This work is supported by the FP7-ICT-2011 7project PANDORA (Ref 288273) funded by the European Commission, two projects funded by the Ministry of Economy and Competitiveness of the Spanish Government. RAIMON (Ref CTM2011-29691-C02-02) and NICOLE (Ref TIN2014-55710-R)
Origin:: © Pattern Recognition, 2017, vol. 61, p. 1-14; Articles publicats (D-ATC)
Idioma:: English
Relationship:: info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patcog.2016.07.008; info:eu-repo/semantics/altIdentifier/issn/0031-3203; info:eu-repo/grantAgreement/MICINN//CTM2011-29691-C02-02/ES/ROBOT AUTONOMO SUBMARINO PARA LA INSPECCION Y MONITORIZACION DE EXPLOTACIONES DE ACUICULTURA MARINA/; info:eu-repo/grantAgreement/MINECO//TIN2014-55710-R/ES/HERRAMIENTAS DE NEUROIMAGEN PARA MEJORAR EL DIAGNOSIS Y EL SEGUIMIENTO CLINICO DE LOS PACIENTES CON ESCLEROSIS MULTIPLE/; info:eu-repo/grantAgreement/EC/FP7/288273/EU/Persistent Autonomy through Learning, Adaptation, Observation and Re-planning/PANDORA
Author/Producer:: Muhammad Habib, Mahmood; Diez, Yago; Salvi, Joaquim; Lladó Bardera, Xavier
Editor:: Elsevier
Other collaborators/producers:: Ministerio de Ciencia e Innovación (Espanya); Ministerio de Economía y Competitividad (Espanya)
Rights:: Tots els drets reservats; info:eu-repo/semantics/embargoedAccess
Date:: info:eu-repo/date/embargoEnd/2026-01-01; 2017-01
Resource type:: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion
Format:: application/pdf

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1. < dc:title > A collection of challenging motion segmentation benchmark datasets </ dc:title >
2. < dc:creator > Muhammad Habib, Mahmood </ dc:creator >
3. < dc:creator > Diez, Yago </ dc:creator >
4. < dc:creator > Salvi, Joaquim </ dc:creator >
5. < dc:creator > Lladó Bardera, Xavier </ dc:creator >
6. < dc:contributor > Ministerio de Ciencia e Innovación (Espanya) </ dc:contributor >
7. < dc:contributor > Ministerio de Economía y Competitividad (Espanya) </ dc:contributor >
8. < dc:subject > Imatges -- Processament </ dc:subject >
9. < dc:subject > Image processing </ dc:subject >
10. < dc:subject > Imatges -- Segmentació </ dc:subject >
11. < dc:subject > Imaging segmentation </ dc:subject >
12. < dc:subject > Visió per ordinador </ dc:subject >
13. < dc:subject > Computer vision </ dc:subject >
14. < dc:description > An in-depth analysis of computer vision methodologies is greatly dependent on the benchmarks they are tested upon. Any dataset is as good as the diversity of the true nature of the problem enclosed in it. Motion segmentation is a preprocessing step in computer vision whose publicly available datasets have certain limitations. Some databases are not up-to-date with modern requirements of frame length and number of motions, and others do not have ample ground truth in them. In this paper, we present a collection of diverse multifaceted motion segmentation benchmarks containing trajectory- and region-based ground truth. These datasets enclose real-life long and short sequences, with increased number of motions and frames per sequence, and also real distortions with missing data. The ground truth is provided on all the frames of all the sequences. A comprehensive benchmark evaluation of the state-of-the-art motion segmentation algorithms is provided to establish the difficulty of the problem and to also contribute a starting point. All the resources of the datasets have been made publicly available at http://dixie.udg.edu/udgms/ </ dc:description >
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1. < title > A collection of challenging motion segmentation benchmark datasets </ title >
2. < creator > Muhammad Habib, Mahmood </ creator >
3. < creator > Diez, Yago </ creator >
4. < creator > Salvi, Joaquim </ creator >
5. < creator > Lladó Bardera, Xavier </ creator >
6. < contributor > Ministerio de Ciencia e Innovación (Espanya) </ contributor >
7. < contributor > Ministerio de Economía y Competitividad (Espanya) </ contributor >
8. < subject > Imatges -- Processament </ subject >
9. < subject > Image processing </ subject >
10. < subject > Imatges -- Segmentació </ subject >
11. < subject > Imaging segmentation </ subject >
12. < subject > Visió per ordinador </ subject >
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14. < description > An in-depth analysis of computer vision methodologies is greatly dependent on the benchmarks they are tested upon. Any dataset is as good as the diversity of the true nature of the problem enclosed in it. Motion segmentation is a preprocessing step in computer vision whose publicly available datasets have certain limitations. Some databases are not up-to-date with modern requirements of frame length and number of motions, and others do not have ample ground truth in them. In this paper, we present a collection of diverse multifaceted motion segmentation benchmarks containing trajectory- and region-based ground truth. These datasets enclose real-life long and short sequences, with increased number of motions and frames per sequence, and also real distortions with missing data. The ground truth is provided on all the frames of all the sequences. A comprehensive benchmark evaluation of the state-of-the-art motion segmentation algorithms is provided to establish the difficulty of the problem and to also contribute a starting point. All the resources of the datasets have been made publicly available at http://dixie.udg.edu/udgms/ </ description >
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OAI Repository