AI-driven activity recognition has been used to record emotions of wild animals from walking style, it can also estimate how many people are in a room and it can create videos from start and end frames alone. But that is not all this invention is good for.

Scientists at the University of Illinois, Politecnico di Milano in Italy and the University of California, Davis propose a statistical framework for identifying wild animal group behavior. They say that in preliminary experiments, it exhibits significantly better classification accuracy compared with baseline methods.

 "Understanding animal behavior is central to answering the fundamental question of why animals do (including humans) do what they do," wrote the coauthors. "Recently, biologist[s] started to use wearable technologies, such as GPS, accelerometers, and radio sensors, to track animals and their activities. However, the collected raw data are not human-interpretable and needs to be processed to extract behavioral patterns ... Activity recognition models can be used to learn the relations between the raw time series and the behavioral annotations collected through observations or other modalities."

The researchers explained that time series classification is most often tackled using temporal sequence analysis or machine learning. The former is based on an explicit description of the raw signals, while the latter automatically infers features from input data. The researchers' approach employs a two-step sequence analysis process: First, they select the best global temporal resolution for a given corpus, and then they encode social relations among animal groups by extracting topological and relational components relevant for classification.

The researchers' sourced a publicly available data set of baboon group activities containing 26 animals tracked for 35 days for their experiment. They defined the baboons' social network based on the proximity, such that baboons passing within two meters of each other were considered to have interacted. They reported that their approach achieved accuracy roughly 10% better than previous methods, and adding social information resulted in a 7% improvement over the initial results.

 "Our evaluation on a real-world data set shows that the proposed framework better identifies the complex behavioral dynamics of groups of wild animals," wrote the coauthors, who say they plan to include other data sets in future work. "We are currently working on extending the temporal resolution step to a more dynamic approach allowing varying temporal steps, which will allow to better identify the critical components of each different behaviors."