Non intrusive monitoring of animals in the wild is possible using camera trapping framework, which uses cameras triggered by sensors to take a burst of images of animals in their habitat. However camera trapping framework produces a high volume of data (in the order on thousands or millions of images), which must be analyzed by a human expert. In this work, a method for animal species identification in the wild using very deep convolutional neural networks is presented. Multiple versions of the Snapshot Serengeti dataset were used in order to probe the ability of the method to cope with different challenges that camera-trap images demand. The method reached 88.9% of accuracy in Top-1 and 98.1% in Top-5 in the evaluation set using a residual network topology. Also, the results show that the proposed method outperforms previous approximations and proves that recognition in camera-trap images can be automated.

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Die Einflüsse der Prozessparameter auf den Ablauf der Pyrolysereaktionen und die Verteilung der Produkte Pyrolysat, Flüssigkeit und Gas bei der Pyrolyse von Ölpalmschalen wird durch thermogravimetrische Untersuchungen bestimmt. Diese Untersuchungen erlauben es, Versuchsbedingungen zu ermitteln, bei denen die Pyrolyse im chemisch bestimmten Bereich abläuft.