Existing pedestrian segmentation and detection methods often show a significant drop in performance when heavy occlusion and deformation happen because most approaches rely on holistic modeling. Unlike many previous deep models that directly learn a holistic detector, in this paper, we introduce a pedestrian instance segmentation method with a prior structure of semantic parts named Part Mask R-CNN. Based on pedestrian parts’ proportion structure, process the original dataset annotations and then generate parts annotations as prior. By combining the semantic part branch with other classic detection and segmentation branches, the network learns more about pedestrian instances. Besides, we get such a more accurate pedestrian instance segmentation model without any artificial annotations. By extensive evaluations on the Cityscapes dataset, the results demonstrate that the proposed method can improve approaches such as Mask R-CNN, inaccuracy on pedestrian single class instance segmentation.

当发生严重遮挡和变形时，现有的行人分割和检测方法通常会表现出显着的性能下降，因为大多数方法依赖于整体建模。与之前许多直接学习整体检测器的深度模型不同，在本文中，我们介绍了一种行人实例分割方法，该方法具有名为 Part Mask R-CNN 的语义部分先验结构。根据行人部位的比例结构，对原始数据集标注进行处理，然后按照先验生成部位标注。通过将语义部分分支与其他经典检测和分割分支相结合，网络可以更多地了解行人实例。此外，我们在没有任何人工注释的情况下获得了这样一个更准确的行人实例分割模型。通过对 Cityscapes 数据集的广泛评估，