  Crowd management technologies that leverage computer vision are widespread in
contemporary times. There exists many security-related applications of these
methods, including, but not limited to: following the flow of an array of
people and monitoring large gatherings. In this paper, we propose an accurate
monitoring system composed of two concatenated convolutional deep learning
architectures. The first part called Front-end, is responsible for converting
bi-dimensional signals and delivering high-level features. The second part,
called the Back-end, is a dilated Convolutional Neural Network (CNN) used to
replace pooling layers. It is responsible for enlarging the receptive field of
the whole network and converting the descriptors provided by the first network
to a saliency map that will be utilized to estimate the number of people in
highly congested images. We also propose to utilize a genetic algorithm in
order to find an optimized dilation rate configuration in the back-end. The
proposed model is shown to converge 30\% faster than state-of-the-art
approaches. It is also shown that it achieves 20\% lower Mean Absolute Error
(MAE) when applied to the Shanghai data~set.
