Sciweavers

Share
ICCV
2009
IEEE

Bayesian Poisson regression for crowd counting

8 years 4 months ago
Bayesian Poisson regression for crowd counting
Poisson regression models the noisy output of a counting function as a Poisson random variable, with a log-mean parameter that is a linear function of the input vector. In this work, we analyze Poisson regression in a Bayesian setting, by introducing a prior distribution on the weights of the linear function. Since exact inference is analytically unobtainable, we derive a closed-form approximation to the predictive distribution of the model. We show that the predictive distribution can be kernelized, enabling the representation of non-linear log-mean functions. We also derive an approximate marginal likelihood that can be optimized to learn the hyperparameters of the kernel. We then relate the proposed approximate Bayesian Poisson regression to Gaussian processes. Finally, we present experimental results using Bayesian Poisson regression for crowd counting from low-level features.
Antoni B. Chan, Nuno Vasconcelos
Added 18 Feb 2011
Updated 18 Feb 2011
Type Journal
Year 2009
Where ICCV
Authors Antoni B. Chan, Nuno Vasconcelos
Comments (0)
books