Detection and Counting of Animals in CameraTrap Images using Faster R-Cnn and Data Augmentation Techniques

Abstract

Camera traps are used to recover images of animals in their habitats to help in the conservation of fauna. Millions of images are captured by camera traps and extracting information from these data delays and consumes enough resources so sometimes millions of images cannot be used due to lack of resources. That is why researchers have proposed solution approaches using Convolutional Neural Networks (CNNs) and object detection models to be able to automate the retrieval of information from these images. We used Faster R-CNN and data augmentation techniques on Gold Standard Snapshot Serengeti Dataset to detect animals in images and count them. The performances of the two models (the one trained on the original dataset and the one trained on the augmented dataset) were compared to show the importance of having more data for this task. Using the augmented dataset, we trained our model which reached an accuracy of 98.26% for classification of the proposed regions, an accuracy of 79.55% for counting the species present on the images and a mAP of 95.3%. For future work, the model can be trained to recognize the actions and characteristics of animals and tuned to be more efficient for counting task.