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Towards Automatic Detection of Animals in Camera-Trap Images

: Loos, Alexander; Weigel, Christin; Koehler, Mona


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society; European Association for Speech, Signal and Image Processing -EURASIP-:
26th European Signal Processing Conference, EUSIPCO 2018 : 3-7 September 2018, Roma, Italy
Piscataway, NJ: IEEE, 2018
ISBN: 978-9-0827-9701-5
ISBN: 978-90-827970-0-8
ISBN: 978-1-5386-3736-4
ISBN: 978-90-827970-1-5
European Signal Processing Conference (EUSIPCO) <26, 2018, Roma>
Conference Paper
Fraunhofer IDMT ()
animal; biology computing; camera; camera-trap image; computer vision; deep-learning based object detector; detector; image classification; learning (artificial intelligence); main species; metadata; object detection; object recognition; population size; remote camera device; Serengeti animal; sociology; statistic; wildlife monitoring; zoology

In recent years the world's biodiversity is declining on an unprecedented scale. Many species are endangered and remaining populations need to be protected. To overcome this agitating issue, biologist started to use remote camera devices for wildlife monitoring and estimation of remaining population sizes. Unfortunately, the huge amount of data makes the necessary manual analysis extremely tedious and highly cost intensive. In this paper we re-train and apply two state-of-the-art deep-learning based object detectors to localize and classify Serengeti animals in camera-trap images. Furthermore, we thoroughly evaluate both algorithms on a self-established dataset and show that the combination of the results of both detectors can enhance overall mean average precision. In contrast to previous work our approach is not only capable of classifying the main species in images but can also detect them and therefore count the number of individuals which is in fact an important information for biologists, ecologists, and wildlife epidemiologists.