基于优化的Inceptio-ResNet-A模块与Gradient Boosting的人群计数方法

针对人群计数问题，基于优化InceptionResNetA模块，使用集成学习中的Gradient Boosting方法提出了一种可用于稀疏人群和密集人群的人群计数方法, 并给出此方法实现的具体细节.通过在三个公开数据集和真实场景（含光照和视角变化）中进行测试，检验了该方法对于光照、人群密度、视角等变化的鲁棒性.实验结果表明，该方法对于以上变化具有较强的鲁棒性，并且相比于之前的人群计数方法在准确性和稳定性方面具有更好的性能.

A Method of Crowd Counting Based on Improved InceptionResNetA Module with Gradient Boosting

To count the pedestrians in the scenarios with the sparse or dense crowd, a network based on the improved Inception-ResNet-A module is proposed, which is trained with the gradient boosting method of ensemble learning, and the details of the proposed method are given. Besides, a dataset collected in a real scenario, which contains illumination and camera view changes, and other three public datasets are used to evaluate the robustness of the proposed method in terms of illumination, population density, and camera view changes. The experimental results show that the proposed method is robust to the aforementioned changes. In addition, the proposed method favorably outperforms the state-of-the-art approaches in terms of accuracy and stability.