Dilated-Scale-Aware Category-Attention ConvNet for Multi-Class Object Counting

Object counting aims to estimate the number of objects in images. The leading counting approaches focus on single-category counting tasks and achieve impressive performance. Nevertheless, there are multiple categories of objects in real scenes. Multi-class object counting expands the scope of application of object counting tasks. The multi-target detection task can achieve multi-class object counting in some scenarios. However, it requires the dataset annotated with bounding boxes. Compared with the point-level annotations used in mainstream object counting issues, the box-level annotations are more difficult to be obtained. In this paper, we propose a simple yet efficient counting network based on point-level annotations. Specifically, we first change the traditional estimated density map from one to the number of categories to achieve multi-class object counting. Since all categories of objects use the same feature extractor, their features will interfere mutually in the shared feature space. We further design a multi-mask structure to suppress the negative interaction among objects. Extensive experiments on the challenging benchmarks demonstrate that the proposed method achieves state-of-the-art counting performance. The code is available at https://github.com/PRIS-CV/DSACA.

Automated summary

The study proposed a counting network based on point-level annotations achieving multi-class object counting. By expanding the traditional predicted density map to multiple category counts and suppressing negative interactions among objects, the method achieved state-of-the-art counting performance.