A benchmark for computational analysis of animal behavior, using animal-borne tags

arXiv:2305.10740v2 Announce Type: replace
Abstract: Animal-borne sensors ('bio-loggers') can record a suite of kinematic and environmental data, which can elucidate animal ecophysiology and improve conservation efforts. Machine learning techniques are used for interpreting the large amounts of data recorded by bio-loggers, but there exists no common framework for comparing the different machine learning techniques in this domain. To address this, we present the Bio-logger Ethogram Benchmark (BEBE), a collection of datasets with behavioral annotations, as well as a modeling task …

analysis arxiv behavior benchmark computational cs.lg q-bio.qm tags type