Trending Paths: A New Semantic-Level Metric for Comparing Simulated and Real Crowd Data

Authors

He Wang (Disney Research Los Angeles)
Jan Ondrej (Disney Research Los Angeles)
Carol O’Sullivan (Disney Research Los Angeles)

IEEE Transactions on Visualization and Computer Graphics (TVCG) 2017

January 16, 2017

(a) A video screenshot from a train station; (b) 1000 tracklets (randomly selected from 19999); (c-g) The five orientation subdomains of the top pattern as location-orientation distributions. Inset shows discretization of the orientation, with black representing zero velocity.

Although a large variety of crowd simulation methods exist, choosing the best algorithm for specific scenarios or applications remains a challenge. Human behavior is very complex, and no one algorithm can be a magic bullet for every situation. Furthermore, different parameter settings for any given method can give widely varying results. Subjective user studies can be useful to determine perceived realism or aesthetic qualities, but more objective methods are often needed to determine the fidelity and/or predictive power of a given simulation method with respect to real human behaviors. The hierarchical and heterogeneous nature of human crowd behaviors makes it very difficult to find a definitive set of evaluation rules or empirical metrics. Therefore, data-driven evaluation methods are particularly useful for this purpose.

Previous data-driven methods tend to focus on comparisons between high-level global features such as densities and exit rates, or low-level data such as individual trajectories. In the former case, the results are often too general and do not reflect the heterogeneity of human behaviors, and in the latter case, the results are too specific to the exact scenario recorded. Based on [1], we propose a data-driven approach to crowd evaluation based on exposing the latent patterns of behavior that exist in both real and simulated data, which offers a compromise between these two extremes that takes both the global and local properties of crowd motion into account in order to facilitate a comprehensive qualitative and quantitative analysis of the data. Different from existing methods, the input of our method is not limited to trajectory data, and it also holds less assumptions on the data and is more robust to noise. Finally, we provide in-depth evaluations to show that our metric is a good alternative capturing unique information which is difficult for existing approaches.

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