@article{ref1,
title="Pedestrian detection by novel axis-line representation and regression pattern",
journal="Sensors (Basel)",
year="2021",
author="Zhang, Mengxue and Liu, Qiong",
volume="21",
number="10",
pages="-",
abstract="The pattern of bounding box representation and regression has long been dominant in CNN-based pedestrian detectors. Despite the method's success, it cannot accurately represent location, and introduces unnecessary background information, while pedestrian features are mainly located in axis-line areas. Other object representations, such as corner-pairs, are not easy to obtain by regression because the corners are far from the axis-line and are greatly affected by background features. In this paper, we propose a novel detection pattern, named Axis-line Representation and Regression (ALR), for pedestrian detection in road scenes. Specifically, we design a 3-d axis-line representation for pedestrians and use it as the regression target during network training. A line-box transformation method is also proposed to fit the widely used box-annotations. Meanwhile, we explore the influence of deformable convolution base-offset on detection performance and propose a base-offset initialization strategy to further promote the gain brought by ALR. Notably, the proposed ALR pattern can be introduced into both anchor-based and anchor-free frameworks. We validate the effectiveness of ALR on the Caltech-USA and CityPersons datasets. Experimental results show that our approach outperforms the baseline significantly through simple modifications and achieves competitive accuracy with other methods without bells and whistles.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s21103312",
url="http://dx.doi.org/10.3390/s21103312"
}