Recognition of Mammal Genera on Camera-Trap Images Using Multi-layer Robust Principal Component Analysis and Mixture Neural Networks

The segmentation and classification of animals from camera-trap images is a difficult task due to the conditions under which the images are taken. This work presents a method for recognizing mammal genera from camera-trap images. Our method uses Multi-Layer Robust Principal Component Analysis (RPCA) for segmenting, Convolutional Neural Networks (CNNs) for extracting features, Least Absolute Shrinkage and Selection Operator (LASSO) for selecting features, and Artificial Neural Networks (ANNs) or Support Vector Machines (SVM) for classifying mammal genera present in the Colombian forest. Our classification method mixes the features of several CNNs. We evaluated our method with the camera-trap images from the Instituto de Investigación de Recursos Biológicos Alexander von Humboldt. We obtained an accuracy of 92.65% classifying 8 mammal genera and a False Positive (FP) class, using automatic-segmented images. On the other hand, we reached 90.32% of accuracy classifying 10 mammal genera, using ground-truth images only. Unlike all previous works, we confront the animal segmentation and genera classification on the camera-trap framework. This method shows a new approach toward a fully-automatic detection of animals from camera-trap images.