While methods for object detection and tracking are well-developed for the purposes of human and vehicle identification, animal identification and re-identification from images and video is lagging behind. There is no clarity as to which object detection methods will work well on animal data. Here we compare two state-of-the art methods which output bounding boxes: the MMDetector and the UniTrack video tracker. Both methods were chosen for their high ranking on benchmark data sets. Using a bespoke pre-annotated database of five videos, we calculated the Average Precision (AP) of the outputs from the two methods. We propose a combination method to fuse the outputs of MMDetection and UniTrack and demonstrate that the proposed method is capable of outperforming both.