L. F. Gonzalez, G. A. Montes, E. Puig, K. Johnson, K. J. Mengersen et al., Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation, Sensors, vol.25, issue.1, p.97, 2016.
DOI : 10.1016/j.envsci.2015.07.024

F. Maire, L. M. Alvarez, and A. Hodgson, Automating Marine Mammal Detection in Aerial Images Captured During Wildlife Surveys: A Deep Learning Approach, Australasian Joint Conf. on Artificial Intelligence, pp.379-385, 2015.
DOI : 10.1007/978-3-319-26350-2_33

D. J. Klein, M. W. Mckown, and B. R. Tershy, Deep learning for large scale biodiversity monitoring, Bloomberg Data for Good Exchange Conference, 2015.

M. Laroze, L. Courtrai, and S. Lefèvre, Human Detection from Aerial Imagery for Automatic Counting of Shellfish Gatherers, Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2016.
DOI : 10.5220/0005786506640671
URL : https://hal.archives-ouvertes.fr/hal-01253867

B. Settles, Active learning literature survey, Computer Sciences, 2009.

D. Tuia, M. Volpi, L. Copa, M. Kanevski, and J. Munoz-mari, A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification, IEEE Journal of Selected Topics in Signal Processing, vol.5, issue.3, 2011.
DOI : 10.1109/JSTSP.2011.2139193

S. Vijayanarasimhan and K. Grauman, Large-Scale Live Active Learning: Training Object Detectors with Crawled Data and Crowds, International Journal of Computer Vision, vol.36, issue.2, pp.97-114, 2014.
DOI : 10.1109/TPAMI.2013.121

S. Sivaraman and M. M. Trivedi, A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking, IEEE Transactions on Intelligent Transportation Systems, vol.11, issue.2, pp.267-276, 2010.
DOI : 10.1109/TITS.2010.2040177

T. Luo, K. Kramer, D. B. Goldgof, L. O. Hall, S. Samson et al., Active learning to recognize multiple types of plankton, Journal of Machine Learning Research, vol.6, pp.589-613, 2005.

Y. Abramson and Y. Freund, Active learning for visual object detection, 2006.

B. and L. Saux, Interactive Design of Object Classifiers in Remote Sensing, 2014 22nd International Conference on Pattern Recognition, 2014.
DOI : 10.1109/ICPR.2014.444
URL : https://hal.archives-ouvertes.fr/hal-01070388

D. P. Papadopoulos, J. R. Uijlings, F. Keller, and V. Ferrari, We Don???t Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.854-863, 2016.
DOI : 10.1109/CVPR.2016.99

A. D. Sarma, A. Jain, A. Nandi, A. Parameswaran, and J. Widom, Surpassing humans and computers with jellybean: crowd-vision-hybrid counting algorithms, AAAI Conf. on Human Computation and Crowdsourcing, 2015.

S. Tong and D. Koller, Support vector machine active learning with applications to text classification, Journal of Machine Learning Research, vol.2, pp.45-66, 2002.