Each Step its Own AI
Rather than use a single AI to perform the entire process of image processing, each step is performed with its own AI. TrapTagger uses 3 separate AI algorithms – one to detect and count animals, one to classify their species, and one to identify individuals.
Animal Detection & Counting
However, MegaDetector can sometimes mistake odd-looking branches, other camera traps, and other anomalies that it has not encountered before, as animals. These mistakes tend to be static and occur in the same location across a camera’s images and can thus be easily detected and removed. This further reduces the number of empty images that need to be examined.
Importantly, the MegaDetector model has been trained on international camera trap data from a very wide variety of ecosystems, and thus performs well on all animal species, in all environments. As such, data from biomes where we do not have a species classifier can still take advantage of this first processing step to drastically reduce the number of images that need to be manually annotated through the platform.
Ideally, one would like an AI model capable of accurately identifying every single animal species on the planet, 100% of the time. However, such a classifier is just not realistic at this point in time, and more real-world models of such magnitude simply do not generate sufficient accuracy for stringent applications such as academic studies. Instead, we focus on creating regional classifiers, training them on our user’s data as soon as enough is available, thus slowly expanding our reach little by little.
Our Current Classifiers
Due to our African roots, we offer both a general sub-Saharan species classifier and a more-specialised southern-African classifier for your use within TrapTagger. However, we are in the process of expanding this offering to include a general European species classifier, and a first-generation South-American classifier. If you have a database of annotated camera trap images from these regions (or anywhere else for that matter), we ask you to please share this with us to help improve the performance of these classifiers and help the community at large.
We Can Train One For You!
Don’t let the lack of a species classifier for your particular region deter you. The automatic species-classification step is entirely optional, and you will still be able to use our highly parallelised and optimised manual tagging interface to rapidly annotate your empty-image-free datasets. Then, once you have sufficient annotated data, we can train a model for your ecosystem, allowing you to have all the benefits of automatically-annotated datasets going forward.
Or Bring Your Own!
TrapTagger is set up to easily integrate third-party species classifiers, which means you can bring your own classifier to use within the TrapTagger environment, and even share it with everybody else! This provides a great opportunity for collaboration between ecology/zoology departments and their computer-science counterparts – and sharing these creations to help the planet.
Unfortunately, HotSpotter is far from the point of being able to reliably perform the task of automatic individual identification by itself. As such, it is used to calculate a similarity score between animals, which in conjunction with our own heuristic algorithm, informs which sets of images should be manually reviewed to determine if they are indeed from the same individual.
This manual review process has been massively streamlined and automated, to make it as easy as possible. This means that if HotSpotter is known to perform poorly on your particular species of interest, you can still use the platform by choosing to switch it off. Instead using the combination of the heuristic algorithm and the automated workflow to more reliably identify individuals than what can be achieved by hand.
Help us, Help You
If you already have a large amount of camera-trap data, and would like to make use of AI species classification, please contact us, and we can look into training something for you.