SGAT: Shuffle and graph attention based Siamese networks for visual tracking.

ABSTRACT: Siamese-based trackers have achieved excellent performance and attracted extensive attention, which regard the tracking task as a similarity learning between the target template and search regions. However, most Siamese-based trackers do not effectively exploit correlations of the spatial and channel-wise information to represent targets. Meanwhile, the cross-correlation is a linear matching method and neglects the structured and part-level information. In this paper, we propose a novel tracking algorithm for feature extraction of target templates and search region images. Based on convolutional neural networks and shuffle attention, the tracking algorithm computes the similarity between the template and a search region through a graph attention matching. The proposed tracking algorithm ex

SUBMITTER: Wang J 

PROVIDER: S-EPMC9683595 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature