: A comparison between a rendering of real-world crowd data (a), and stills from three different simulation algorithms applied to the same scenario (b-d). Our entropy metric is used to measure the similarity of simulation algorithm to real-world data. A small value of the metric, as in (d), indicates a better match to the data. Differences between the simulations are highlighted with circles. We present an information-theoretic method to measure the similarity between a given set of observed, real-world data and visual simulation technique for aggregate crowd motions of a complex system consisting of many individual agents. This metric uses a two-step process to quantify a simulator's ability to reproduce the collective behaviors of the whole system, as observed in the recorded realworld data. First, Bayesian inference is used to estimate the simulation states which best correspond to the observed data, then a maximum likelihood estimator is used to approximate the prediction errors. This process is iterated using the EM-algorithm to produce a robust, statistical estimate of the magnitude of the prediction error as measured by its entropy (smaller is better). This metric serves as a simulator-to-data similarity measurement. We evaluated the metric in terms of robustness to sensor noise, consistency across different datasets and simulation methods, and correlation to perceptual metrics.
Guy et al. REF proposed models to measure the similarity between a given set of observed (real-world) data, and a visual simulation technique to generate crowd motions.
A statistical similarity measure for aggregate crowd dynamics
TOGS
Computer Science