Visual identification of individual animals that bear unique natural body markings is an important task in wildlife conservation. The photo databases of animal markings grow larger and each new observation has to be matched against thousands of images. Existing photo-identification solutions have constraints on image quality and appearance of the pattern of interest in the image. These constraints limit the use of photos from citizen scientists. We present a novel system for visual re-identification based on unique natural markings that is robust to occlusions, viewpoint and illumination changes. We adapt methods developed for face re-identification and implement a deep convolutional neural network (CNN) to learn embeddings for images of natural markings. The distance between the learned embedding points provides a dissimilarity measure between the corresponding input images. The network is optimized using the triplet loss function and the online semi-hard triplet mining strategy. The proposed re-identification method is generic and not species specific. We evaluate the proposed system on image databases of manta ray belly patterns and humpback whale flukes. To be of practical value and adopted by marine biologists, a re-identification system needs to have a top-10 accuracy of at least 95 . The proposed system achieves this performance standard.
Some methods have been developed for specific species and, while performing well on these, are not easily transferable to other species. High-contrast colour patterns of humpback whale flukes @cite_8 and dolphin dorsal fins @cite_41 are matched by extracting hand-crafted features from corresponding segments obtained by overlaying a grid on a region of interest. This method is not robust to viewpoint changes.
@cite_41 @cite_8
2569469748 2118037900
Ecologists commonly use photo-identification of individual animals to monitor the behaviour, state and health of a population, since it is a cost-effective technique that eliminates the need to physically capture and tag animals. With dolphins, the nicks and notches of the dorsal fin are typically used as the unique identifying features for each individual; however New Zealand common dolphins are relatively unmarked, so most of the population cannot be identified. Here, we investigate how computer vision can be used to extract information from the pigmentation patterns that are typically seen on adult common dolphin dorsal fins. We develop features that are relatively robust to changes in the fin orientation and compare the classification rates of 779 photos of 169 different adult common dolphins. Using pigmentation-based features, we correctly classified individuals 75 of the time, with our top-5 estimates containing the correct dolphin in 86 of the cases. This paper describes current work on a photo-id system for humpback whales. Individuals of this species can be uniquely identified by the light and dark pigmentation patches on their tails (flukes). We developed an interface that assists the user in segmenting the animal's tail from the sea and fitting an affine invariant coordinate grid to it. A numerical feature vector capturing the patch-distribution with respect to the grid is then automatically extracted and used to match the individual against a database of similarly processed images.