Pedestrian Instance Segmentation With Prior Structure Of Semantic Parts

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 RCNN. 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. (C) 2021 Elsevier B.V. All rights reserved.