| dc.description.abstract | As the number of endangered species in the world increases due to human activity, biodiversity is decreasing rapidly. Obtaining knowledge about endangered species is the key to stopping this decline. Artificial intelligence is used to speed up the process of analyzing camera trap data. Identifying individuals of endangered species can offer insights in population numbers and movement patterns. Automatic detection and classification of species is necessary to obtain a dataset that contains a certain endangered species. Such a dataset provides the opportunity to identify individuals within species. This study analyzes the artificial intelligence techniques that can be used for both of these tasks. At this moment, HotSpotter and StripeSpotter are two prominent types of software that can be used for identifying individuals within species. To test which of the two performs best, I applied both types of software to a self-made dataset of snow leopard individuals. Regarding biodiversity, it is important to obtain knowledge about snow leopard populations. Because the snow leopard is an endangered species that as a rare predator is of great importance for preserving biodiversity, I chose to test both types of software on snow leopard individuals. I tested both types of software also on a dataset of zebra individuals to verify my hypothesis. The results show that HotSpotter outperforms StripeSpotter for both datasets. For this reason, my conclusion is that HotSpotter can be used best for identifying individuals within species. | |
