Semantic segmentation has become one of the trending topics in the world of computer vision and deep learning. Recently, due to an increasing demand to solve a semantic segmentation task simultaneously with attribute recognition of objects, a new task named attribute‐aware semantic segmentation has been introduced. Since the task requires to handle pixel‐wise object class estimation with its attributes such as a pedestrian's body orientation, previous works had difficulties to handle ambiguous attributes such as body orientations in object‐level, especially when segmenting the pedestrians with their attributes correctly. This paper proposes the ColAtt‐Net that is an attribute‐aware semantic segmentation model augmented by a column‐wise mask branch to predict the pedestrians' orientations in the horizontal perspective of the input image. We firmly assume that the pedestrians captured by a car‐mounted camera are distributed horizontally so that for each column of the input image, the pedestrian pixels can be labeled with one orientation uniformly. In the proposed method, we split the output of the base semantic segmentation model into two branches; one branch for segmenting the object categories, while the other one, as the novel column‐wise attribute branch, is to map the recognition of pedestrian's orientations that are distributed horizontally. This method successfully enhances the performance of attribute‐aware semantic segmentation by reducing the ambiguity on segmenting the pedestrian's orientation. Improvements on the pedestrian orientation segmentation are confidently shown by the proposed method in the experimental results, both in quantitative and qualitative views. This paper also discusses how the improved performance becomes an advantage in the autonomous driving system. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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ColAtt-Net：减少行人方向对属性感知语义分割任务的歧义

语义分割已成为计算机视觉和深度学习领域的热门话题之一。近年来，由于解决与对象的属性识别同时进行语义分割任务的需求不断增加，引入了一种名为属性感知语义分割的新任务。由于该任务需要使用其属性（例如行人的身体朝向）来处理像素级对象类别估计，因此以前的工作难以处理诸如对象级的身体朝向等含糊属性，尤其是在正确分割行人及其属性时。本文提出了ColAtt-Net，它是一种属性感知的语义分割模型，并通过按列的掩码分支进行了增强，可以在输入图像的水平透视图中预测行人的方向。我们坚决假定车载摄像头捕获的行人是水平分布的，因此对于输入图像的每一列，行人像素都可以用一个方向统一标记。在所提出的方法中，我们将基本语义分割模型的输出分为两个分支。一个分支用于分割对象类别，而另一个分支（如新颖的按列属性分支）将映射对水平分布的行人方向的识别。该方法通过减少对行人方向进行分割的歧义，成功地提高了属性感知语义分割的性能。实验结果可靠地表明了该方法对行人方向分割的改进，在数量和质量上都可以。本文还讨论了改进的性能如何在自动驾驶系统中成为优势。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。