An Improved Dilated Convolutional Network for Herd Counting in Crowded Scenes
This work addresses crowd management for security applications, but it is incremental as it builds on existing dilated CNN methods.
The paper tackled the problem of accurately counting people in crowded scenes by proposing a two-part convolutional network with a dilated CNN and genetic algorithm optimization, achieving 30% faster convergence and 20% lower Mean Absolute Error on the Shanghai dataset.
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.