nach oben

Erschienen in:

2016 | OriginalPaper | Buchkapitel

Counting in the Wild

verfasst von : Carlos Arteta, Victor Lempitsky, Andrew Zisserman

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this paper we explore the scenario of learning to count multiple instances of objects from images that have been dot-annotated through crowdsourcing. Specifically, we work with a large and challenging image dataset of penguins in the wild, for which tens of thousands of volunteer annotators have placed dots on instances of penguins in tens of thousands of images. The dataset, introduced and released with this paper, shows such a high-degree of object occlusion and scale variation that individual object detection or simple counting-density estimation is not able to estimate the bird counts reliably.

To address the challenging counting task, we augment and interleave density estimation with foreground-background segmentation and explicit local uncertainty estimation. The three tasks are solved jointly by a new deep multi-task architecture. Using this multi-task learning, we show that the spread between the annotators can provide hints about local object scale and aid the foreground-background segmentation, which can then be used to set a better target density for learning density prediction. Considerable improvements in counting accuracy over a single-task density estimation approach are observed in our experiments.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks

Nächstes Kapitel A Discriminative Feature Learning Approach for Deep Face Recognition

Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Interactive object counting. In: ECCV (2014)

Chan, A.B., Liang, Z.S.J., Vasconcelos, N.: Privacy preserving crowd monitoring: counting people without people models or tracking. In: CVPR (2008)

Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: CVPR (2009)

Fiaschi, L., Nair, R., Köethe, U., Hamprecht, F.: Learning to count with regression forest and structured labels. In: ICPR (2012)

Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: representing model uncertainty in deep learning. arXiv preprint arXiv:1506.02142 (2015)

Idrees, H., Soomro, K., Shah, M.: Detecting humans in dense crowds using locally-consistent scale prior and global occlusion reasoning. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1986–1998 (2015)CrossRef

Kong, D., Gray, D., Tao, H.: A viewpoint invariant approach for crowd counting. In: ICPR (2006)

Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: NIPS (2010)

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)

10.

Ma, F., Li, Y., Li, Q., Qiu, M., Gao, J., Zhi, S., Su, L., Zhao, B., Ji, H., Han, J.: FaitCrowd: fine grained truth discovery for crowdsourced data aggregation. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2015)

11.

Ma, Z., Yu, L., Chan, A.B.: Small instance detection by integer programming on object density maps. In: CVPR (2015)

12.

Ouyang, R.W., Kaplan, L.M., Toniolo, A., Srivastava, M., Norman, T.: Parallel and streaming truth discovery in large-scale quantitative crowdsourcing. IEEE Trans. Parallel Distrib. Syst. PP(99), 1 (2016)

13.

Penguin research webpage. www.robots.ox.ac.uk/~vgg/research/penguins

14.

Raykar, V.C., Yu, S., Zhao, L.H., Valadez, G.H., Florin, C., Bogoni, L., Moy, L.: Learning from crowds. J. Mach. Learn. Res. 11, 1297–1322 (2010)MathSciNet

15.

Russakovsky, O., Bearman, A.L., Ferrari, V., Li, F.F.: What’s the point: semantic segmentation with point supervision. arXiv preprint arXiv:1506.02106 (2015)

16.

Shao, J., Kang, K., Loy, C.C., Wang, X.: Deeply learned attributes for crowded scene understanding. In: CVPR (2015)

17.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)

18.

Straehle, C., Koethe, U., Hamprecht, F.A.: Weakly supervised learning of image partitioning using decision trees with structured split criteria. In: ICCV (2013)

19.

Van Horn, G., Branson, S., Farrell, R., Haber, S., Barry, J., Ipeirotis, P., Perona, P., Belongie, S.: Building a bird recognition app. and large scale dataset with citizen scientists: the fine print in fine-grained dataset collection. In: CVPR (2015)

20.

Wang, T., Han, B., Collomosse, J.: Touchcut: fast image and video segmentation using single-touch interaction. Comput. Vis. Image Underst. 120, 14–30 (2014)CrossRef

21.

Welinder, P., Branson, S., Perona, P., Belongie, S.J.: The multidimensional wisdom of crowds. In: NIPS (2010)

22.

Whitehill, J., Wu, T.f., Bergsma, J., Movellan, J.R., Ruvolo, P.L.: Whose vote should count more: Optimal integration of labels from labelers of unknown expertise. In: NIPS (2009)

23.

Wolley, C., Quafafou, M.: Learning from multiple naive annotators. In: Zhou, S., Zhang, S., Karypis, G. (eds.) ADMA 2012. LNCS (LNAI), vol. 7713, pp. 173–185. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35527-1_15 CrossRef

24.

Xie, W., Noble, J.A., Zisserman, A.: Microscopy cell counting with fully convolutional regression networks. In: MICCAI 1st Workshop on Deep Learning in Medical Image Analysis (2015)

25.

Xie, W., Noble, J.A., Zisserman, A.: Microscopy cell counting and detection with fully convolutional regression networks. Comput. Methods Biomech. Biomed. Eng. Imaging Visual. 1–10 (2016)

26.

Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: CVPR (2015)

27.

Zooniverse. penguinwatch.org

Titel: Counting in the Wild
verfasst von: Carlos Arteta
Victor Lempitsky
Andrew Zisserman
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46477-0

Electronic ISBN: 978-3-319-46478-7

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46478-7_30

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner