An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting

TL;DR

This paper introduces an innovative multicolumn dilated convolutional network that effectively aggregates multiscale features for perspective-free counting, outperforming existing methods on benchmark datasets.

Contribution

The paper presents a novel aggregation module using dilated filters within a multicolumn CNN to enhance multiscale counting accuracy.

Findings

Outperforms state-of-the-art on multiple datasets

Aggregation with more columns improves counting performance

Dilated filters effectively capture multiscale information

Abstract

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.