公开(公告)号：US11783594B2

公开(公告)日：2023-10-10

申请号：US17267493

申请日：2019-05-16

Applicant: SOUTHEAST UNIVERSITY

Inventor： Xu Li ,  Zhiyong Zheng ,  Kun Wei

IPC: G06V20/58 ,  G06T7/11 ,  G06V20/56 ,  G06V40/10 ,  G06F18/25 ,  G06N3/045 ,  G06V10/764 ,  G06V10/80 ,  G06V10/82 ,  G06V10/44

CPC classification number: G06V20/58 ,  G06F18/253 ,  G06N3/045 ,  G06T7/11 ,  G06V10/454 ,  G06V10/764 ,  G06V10/806 ,  G06V10/82 ,  G06V20/56 ,  G06V40/10

Abstract: The present invention discloses a method for segmenting pedestrians in roadside images using a variable-scale multi-feature fusion convolutional network. It addresses the challenge of significant changes in pedestrian scale by using two parallel convolutional neural networks to extract the local and global features at different scales, and then fusing them to obtain a variable-scale multi-feature fusion convolutional neural network, and this network is trained using roadside pedestrian images to realize accurate pedestrian segmentation, avoiding issues with boundary fuzziness and missing segments commonly found in single-network methods.

公开(公告)号：US20210303911A1

公开(公告)日：2021-09-30

申请号：US17267493

申请日：2019-05-16

Applicant: SOUTHEAST UNIVERSITY

Inventor： Xu LI ,  Zhiyong Zheng ,  Kun Wei

IPC: G06K9/62 ,  G06K9/00 ,  G06T7/11 ,  G06N3/04

Abstract: The present invention discloses a roadside image pedestrian segmentation method based on a variable-scale multi-feature fusion convolutional network. For scenes where the pedestrian scale changes significantly in the intelligent roadside terminal image, this method designs two parallel convolutional neural networks to extract the local and global features of pedestrians at different scales in the image, and then fuses the local features and global features extracted by the first network with the local features and global features extracted by the second network at the same level, and then fuse the fused local features and global features for the second time to obtain a variable-scale multi-feature fusion convolutional neural network, and then train the network and input roadside pedestrian images to realize pedestrian segmentation. The present invention effectively solves the problems that most current pedestrian segmentation methods based on a single network structure are prone to segmentation boundary fuzziness and missing segmentation.