The Iona Skeleton Coast Trans-Frontier Conservation Area (TFCA), straddling the border between Angola and Namibia, has suffered through decades of civil war and poaching. While this history has been detrimental to the community of large mammals in the TFCA, data collected on the mammal populations are insufficient to enable effective management. Survey methods such as aerial counts and community-based monitoring have various shortcomings. Therefore camera trapping, which has become important in surveying wildlife worldwide, could become an essential monitoring tool also for the TFCA. However, camera traps tend to capture huge numbers of images over short periods of time. The cost and time involved in the manual analysis of such large numbers of images is the major limiting factor in camera trapping.
Deep learning-based computer vision methods proposed to date to address this problem were found unsuitable for application to camera trapping in the TFCA, being computationally too expensive, requiring specialised hardware and too much training data, focusing only one species per photograph or relying on static backgrounds between sequential images. On the other hand, the method developed in this study requires only an entry-level computer and relatively little training data while handling multi-species photos with changing backgrounds. It is able to detect and distinguish between humans, vehicles and four large mammal species of importance in the TFCA, namely giraffe, impala, oryx and zebra.