Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 28, Issue -, Pages 1570-1574Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2021.3096119
Keywords
Annotations; Feature extraction; Task analysis; Convolution; Automobiles; Training; Visualization; Multi-class object counting; point-level annotation; dilated-scale-aware module; category-attention module
Categories
Funding
- National Key R&D Program of China [2020AAA0105200, 2019YFF0303300, 2019YFF0303302]
- National Natural Science Foundation of China (NSFC) [61922015, 61773071, U19B2036]
- Beijing Natural Science Foundation Project [Z200002]
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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.
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.
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