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We have created a large diverse set of cars from overhead images, which are useful for training a deep learner to binary classify, detect and count them.
Overhead imagery research data set: an annotated data library and tools to aid in the developement of computer vision algorithms
Tanner, F., Colder, B., Pullen, C., Heagy, D., Eppolito, M., Carlan, V., Oertel, C., Sallee, P.: · 2009
Earlier work this paper cites.
Object-based detection and classification of vehicles from high-resolution aerial photography
Holt, A.C., Seto, E.Y.W., Rivard, T., Gong, P.: · 2009
Earlier work this paper cites.
Visual-inertial simultaneous localization, mapping and sensor-to-sensor self-calibration
Kelly, J., Sukhatme, G.S.: · 2009
Earlier work this paper cites.
Learning to count objects in images
Lempitsky, V., Zisserman, A.: · 2010
Earlier work this paper cites.
Utah 2012 HRO 6 inch orthophotography data. http://gis.utah.gov/data/aerial-photography/
Utah Automated Geographic Reference Center (AGRC): · 2012
Earlier work this paper cites.
Selwyn 0.125m urban aerial photos index tiles (2012-13). https://data.linz.govt.nz/layer/1926-selwyn-0125m-urban-aerial-photos-2012-13/
Land Information New Zealand (LINZ): · 2012
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Vehicle detection in satellite images by parallel deep convolutional neural networks
Chen, X., Xiang, S., Liu, C.L., Pan, C.H.: · 2013
Earlier work this paper cites.
Multi-source multi-scale counting in extremely dense crowd images
Idrees, H., Saleemi, I., Seibert, C., Shah, M.: · 2013
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2013
Earlier work this paper cites.
Online object tracking: A benchmark
Wu, Y., Yang, M.H., Lim, J.: · 2013
Cited alongside, same era.
Automatic car counting method for unmanned aerial vehicle images
Moranduzzo, T., Melgani, F.: · 2014
Cited alongside, same era.
Interactive object counting
Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
Cited alongside, same era.
Vehicle detection in aerial imegery : A small target detection benchmark
Razakarivony, S., Jurie, F.: · 2015
Cited alongside, same era.
Aerial car detection and urban understanding
Kamenetsky, D., Sherrah, J.: · 2015
Learning to count with deep object features
Segue, S., Pujol, O., Vitria, J.: · 2015
Later among the works it cites.
Deep people counting in extremely dense crowds
Wang, C., Zhang, H., Yang, L., Liu, S., Cao, X.: · 2015
Later among the works it cites.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
Later among the works it cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
Later among the works it cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
Later among the works it cites.
Efficient convolutional patch networks for scene understanding
Brust, C.A., Sickert, S., Simon, M., Rodner, E., Denzler, J.: · 2015
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Cited alongside, same era.
Cross-scene crowd counting via deep convolutional neural networks
Zhang, C., Li, H., Wang, X., Yang, X.: · 2015
Cited alongside, same era.
Convolutional neural network for counting fish in fisheries surveillance video
French, G., Fisher, M.H., Mackiewicz, M., Needle, C.L.: · 2015
Cited alongside, same era.
You only look once: Unified, real-time object detection
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: · 2015
Cited alongside, same era.
WG3 Toronto overhead data. http://www2.isprs.org/commissions/comm3 /wg4/tests.html
International Society for Photogrammetry and Remote Sensing (ISPRS):
Cited in the paper.
WG3 Potsdam overhead data. http://www2.isprs.org/commissions/comm3 /wg4/tests.html
International Society for Photogrammetry and Remote Sensing (ISPRS) and BSF Swissphoto:
Cited in the paper.
WG3 Vaihingen overhead data. http://www2.isprs.org/commissions/comm3 /wg4/tests.html
International Society for Photogrammetry and Remote Sensing (ISPRS) and the German Society of Photogrammetry, Remote Sensing and Geoinformation (DGPF):
Cited in the paper.
Later among the works it cites.
Beyond supply and demand: Making the invisible hand visible
Crawford, J.: · 2016
Closest in time.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, V.V.: · 2016
Closest in time.