Rapid literature mapping on the recent use of machine learning for wildlife imagery

Machine (especially deep) learning algorithms are changing the way wildlife imagery is processed. They dramatically speed up the time to detect, count, and classify animals and their behaviours. Yet, we currently have very few systematic literature surveys on its use in wildlife imagery. Through a literature survey (a ‘rapid’ review) and bibliometric mapping, we explored its use across: 1) species (vertebrates), 2) image types (e.g., camera traps, or drones), 3) study locations, 4) alternative machine learning algorithms, 5) outcomes (e.g., recognition, classification, or tracking), 6) reporting quality and openness, 7) author affiliation, and 8) publication journal types. We found that an increasing number of studies used convolutional neural networks (i.e., deep learning). Typically, studies have focused on large charismatic or iconic mammalian species. An increasing number of studies have been published in ecology-specific journals indicating the uptake of deep learning to transform the detection, classification and tracking of wildlife. Sharing of code was limited, with only 20% of studies providing links to analysis code. Much of the published research and focus on animals came from India, China, Australia, or the USA. There were relatively few collaborations across countries. Given the power of machine learning, we recommend increasing collaboration and sharing approaches to utilise increasing amounts of wildlife imagery more rapidly and transform and improve understanding of wildlife behaviour and conservation. Our survey, augmented with bibliometric analyses, provides valuable signposts for future studies to resolve and address shortcomings, gaps, and biases

Image-Based Recognition of Individual Trouts in the Wild

Individual fish recognition has potentials in applications as fish cultivcation and fishing tourism. Unlike previous research, which either based on physical marker or based on photograph comparison using observers, this paper propose an approach being able to identify individual brown trouts (Salmo trutta) automatically with images taken in the wild. Although big variation in illumination, poses of the trouts, and resolution we validated that just using a small patch taken from the head of the trout, which can minimize the variations, it's possible to recognize individuals automatically. Two methods were proposed based on a local density profile and on a codebook. Both of the methods gave modest recognition accuracy 64.9% and 74% respectively, which compared to random chance at 3.3% is significantly better