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(a) Residual plot of the estimated crowd density to the actual crowd density for the two kernel radii R = 10 m and R = 55 m.
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They particularly emphasized the importance of the real time crowd density information for identifying areas where incidents might be most likely to occur.
This upper limit is depends on the crowd density and decays for larger crowd densities.
It is, however, observable that this maximal value depends on the crowd density and decreases for higher densities.
With a dramatic reduction in crowd density and better access for stewards and police, carnival-goers and carnivalists will breathe a sigh of relief.
The contributions are twofold: first, convolutional neural network is first introduced for crowd density estimation.
[12] state the need for accurate crowd density estimation to correctly asses the criticality of a situation.
The local crowd density alone does not allow for a complete assessment of the criticality of a situation.
For low crowd density situations, pedestrians will be able to maintain free flow speed and are not interrupted by their neighbors.
This implies that there is some predicting power for obtaining a crowd density estimation, and the calibration error is σ = 0.54 m − 2 for R = 10 m and σ = 0.36 m − 2 for R = 55 m, respectively.
In our ongoing research effort, we want to turn pedestrians' smartphones into a reliable sensing tool for measuring the crowd density during city-wide mass gatherings.
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