Sentence examples for crowd density distribution from inspiring English sources

The core functionality of the proposed system is the estimation of crowd density distribution from individual location fixes provided by a subset of users (the ones who have installed the app).

To understand how well we can estimate the crowd density from the distribution of App users, we determine the overall calibration error by calculating the root mean squared error (RMSE) σ as follows: σ = ∑ i N ( ρ Crowd − ρ ˆ Crowd ) 2 N = ∑ i N ( ρ Crowd − ( m k ⋅ ρ User + q k ) ) 2 N. (12).

Based on the findings deduced in the previous section, we introduce and evaluate a methodology to estimate a crowd density from the spatial distribution of App users.

However, the obtained App user distribution does not reflect the actual crowd density.

Our methodology relies on a calibration approach that provides a relation between the distribution of App users and the crowd density.

Ideally, the estimated crowd density ρ ˆ Crowd obtained from the calibrated App user distribution should be identical to the observed crowd density ρ Crowd from the video footage.

Given (1) an initial crowd state (e.g. size, initial distribution), (2) a physical space layout, and (3) a certain general crowd behavior (e.g. everyone heading towards the nearest exit, or most people moving in one direction) such models can predict the evolution of global parameters such as the density distribution, evacuation time or average physical pressure within the crowd.

Therefore, the explanatory power of the obtained distribution is limited as these numbers do not provide direct evidence of the actual crowd density.

The topics cover crowd density estimation, crowd counting, and crowd attribute recognition.

Pedestrians compete frequently at medium crowd density.

The first effect gives a logarithmic relationship between the crowd speed and crowd density.