TY  - JOUR
PY  - 2016//
TI  - Trending paths: a new semantic-level metric for comparing simulated and read crowd data
JO  - IEEE transactions on visualization and computer graphics
A1  - Wang, He
A1  - Ondrej, Jan
A1  - O'Sullivan, Carol
SP  - 1454
EP  - 1464
VL  - 23
IS  - 5
N2  - We propose a new semantic-level crowd evaluation metric in this paper. Crowd simulation has been an active and important area for several decades. However, only recently has there been an increased focus on evaluating the fidelity of the results with respect to real-world situations. The focus to date has been on analyzing the properties of low-level features such as pedestrian trajectories, or global features such as crowd densities. We propose the first approach based on finding semantic information represented by latent Path Patterns in both real and simulated data in order to analyze and compare them. Unsupervised clustering by non-parametric Bayesian inference is used to learn the patterns, which themselves provide a rich visualization of the crowd behavior. To this end, we present a new Stochastic Variational Dual Hierarchical Dirichlet Process (SV-DHDP) model. The fidelity of the patterns is computed with respect to a reference, thus allowing the outputs of different algorithms to be compared with each other and/or with real data accordingly. Detailed evaluations and comparisons with existing metrics show that our method is a good alternative for comparing crowd data at a different level and also works with more types of data, holds fewer assumptions and is more robust to noise.<p />  <p>Language: en</p>
LA  - en
SN  - 1077-2626
UR  - http://dx.doi.org/10.1109/TVCG.2016.2642963
ID  - ref1
ER  -