TY - CHAP
T1 - Animal identification in low quality camera-trap images using very deep convolutional neural networks and confidence thresholds
AU - Gomez-Villa, Alexander
AU - Diez, German
AU - Salazar, Augusto
AU - Diaz, Angelica
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016/12/10
Y1 - 2016/12/10
N2 - Monitoring animals in the wild without disturbing them is possible using camera trapping framework. Automatic triggered cameras, which take a burst of images of animals in their habitat, produce great volumes of data, but often result in low image quality. This high volume data must be classified by a human expert. In this work a two step classification is proposed to get closer to an automatic and trustfully camera-trap classification system in low quality images. Very deep convolutional neural networks were used to distinguish images, firstly between birds and mammals, secondly between mammals sets. The method reached 97.5%97.5% and 90.35%90.35% in each task. An alleviation mode using a confidence threshold of automatic classification is proposed, allowing the system to reach 100%100% of performance traded with human work.
AB - Monitoring animals in the wild without disturbing them is possible using camera trapping framework. Automatic triggered cameras, which take a burst of images of animals in their habitat, produce great volumes of data, but often result in low image quality. This high volume data must be classified by a human expert. In this work a two step classification is proposed to get closer to an automatic and trustfully camera-trap classification system in low quality images. Very deep convolutional neural networks were used to distinguish images, firstly between birds and mammals, secondly between mammals sets. The method reached 97.5%97.5% and 90.35%90.35% in each task. An alleviation mode using a confidence threshold of automatic classification is proposed, allowing the system to reach 100%100% of performance traded with human work.
UR - https://www.scopus.com/pages/publications/85007028227
U2 - 10.1007/978-3-319-50835-1_67
DO - 10.1007/978-3-319-50835-1_67
M3 - Chapter
AN - SCOPUS:85007028227
SN - 9783319508344
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 747
EP - 756
BT - Advances in Visual Computing - 12th International Symposium, ISVC 2016, Proceedings
A2 - Bebis, George
A2 - Parvin, Bahram
A2 - Skaff, Sandra
A2 - Iwai, Daisuke
A2 - Boyle, Richard
A2 - Koracin, Darko
A2 - Porikli, Fatih
A2 - Scheidegger, Carlos
A2 - Entezari, Alireza
A2 - Min, Jianyuan
A2 - Sadagic, Amela
A2 - Isenberg, Tobias
ER -