We propose the use of dilated filters to construct an aggregation module in a multicolumn convolutional neural network for perspective-free counting. Counting is a common problem in computer vision (e.g. traffic on the street or pedestrians in a crowd). Modern approaches to the counting problem involve the production of a density map via regression whose integral is equal to the number of objects in the image. However, objects in the image can occur at different scales (e.g. due to perspective effects) which can make it difficult for a learning agent to learn the proper density map. While the use of multiple columns to extract multiscale information from images has been shown before, our approach aggregates the multiscale information gathered by the multicolumn convolutional neural network to improve performance. Our experiments show that our proposed network outperforms the state-of-the-art on many benchmark datasets, and also that using our aggregation module in combination with a higher number of columns is beneficial for multiscale counting.

本文提出利用扩张滤波器来构建聚合模块，以实现无透视计数的多列卷积神经网络。实验证明，我们的方法在许多基准数据集上胜过现有技术，并且结合更高数量的列来使用我们的聚合模块对于多尺度计数是有益的。

一个聚合的多列扩张卷积神经网络用于无视角计数