Open AccessTemplate Features Guided Siamese Keypoints Detection for Visual Object Tracking
Author(s) -
Ershen Wang,
Donglei Wang,
Yufeng Huang,
Pingping Qu,
Tao Pang,
Shanjia Xu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1802/4/042104
Subject(s) - subnetwork , artificial intelligence , computer science , computer vision , tracking (education) , hourglass , face (sociological concept) , object (grammar) , eye tracking , pattern recognition (psychology) , psychology , pedagogy , social science , computer security , archaeology , sociology , history
Visual object tracking is one of the challenging tasks in computer vision. Among tracking tasks, accurately state estimation is the main challenge. Most current methods simply adopt multi-scale searches or anchors to estimate the state, which need many hyper-parameters and complex calculations. To face this challenge, we propose an anchor-free tracking framework based on Siamese network. The proposed framework consists of template features guided Siamese subnetwork and keypoints detection subnetwork. We take simplified hourglass network as backbone in Siamese subnetwork to improve the tracking efficiency and the prediction of corners around target instead of the prediction of target in keypoints detection subnetwork. We experiment our approach on OTB-2015 and VOT-2016, and our approach acquire the best precision of 0.841 on OTB-2015 and runs at 39 FPS.