2018/08/01 by Gianluca Mastrantonio, Mastrantonio, Gianluca, Clara Grazian +5
Computer Science · Engineering · Environmental Science · #Advanced Chemical Sensor Technologies #Applications (stat.AP) #FOS: Computer and information sciences #Video Surveillance and Tracking Methods #Wildlife Ecology and Conservation
paper · pdf · doi:10.48550/arxiv.1808.00436
openalex publication_date 2018/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Improved communication systems, shrinking battery sizes and the price drop of\ntracking devices have led to an increasing availability of trajectory tracking\ndata. These data are often analyzed to understand animal behavior.\n In this work, we propose a new model for interpreting the animal movent as a\nmixture of characteristic patterns, that we interpret as different behaviors.\nThe probability that the animal is behaving according to a specific pattern, at\neach time instant, is non-parametrically estimated using the Logistic-Gaussian\nprocess. Owing to a new formalization and the way we specify the\ncoregionalization matrix of the associated multivariate Gaussian process, our\nmodel is invariant with respect to the choice of the reference element and of\nthe ordering of the probability vector components. We fit the model under a\nBayesian framework, and show that the Markov chain Monte Carlo algorithm we\npropose is straightforward to implement.\n We perform a simulation study with the aim of showing the ability of the\nestimation procedure to retrieve the model parameters. We also test the\nperformance of the information criterion we used to select the number of\nbehaviors. The model is then applied to a real dataset where a wolf has been\nobserved before and after procreation. The results are easy to interpret, and\nclear differences emerge in the two phases.\n