Author:
Zhang Jianming,Huang Benben,Ye Zi,Kuang Li-Dan,Ning Xin
Abstract
AbstractRecently, object trackers based on Siamese networks have attracted considerable attentions due to their remarkable tracking performance and widespread application. Especially, the anchor-based methods exploit the region proposal subnetwork to get accurate prediction of a target and make great performance improvement. However, those trackers cannot capture the spatial information very well and the pre-defined anchors will hinder robustness. To solve these problems, we propose a Siamese-based anchor-free object tracking algorithm with multiscale spatial attentions in this paper. Firstly, we take ResNet-50 as the backbone network to generate multiscale features of both template patch and search regions. Secondly, we propose the spatial attention extraction (SAE) block to capture the spatial information among all positions in the template and search region feature maps. Thirdly, we put these features into the SAE block to get the multiscale spatial attentions. Finally, an anchor-free classification and regression subnetwork is used for predicting the location of the target. Unlike anchor-based methods, our tracker directly predicts the target position without predefined parameters. Extensive experiments with state-of-the-art trackers are carried out on four challenging visual object tracking benchmarks: OTB100, UAV123, VOT2016 and GOT-10k. Those experimental results confirm the effectiveness of our proposed tracker.
Funder
the National Natural Science Foundation of China
the Postgraduate Training Innovation Base Construction Project of Hunan Province
the Basic Research Fund of Zhongye Changtian International Engineering Co., Ltd.
the Natural Science Foundation of Hunan Province of China
Publisher
Springer Science and Business Media LLC
Cited by
26 articles.
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