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A strong feature representation for siamese network tracker
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DOI：10.1007/s11042-020-09164-2.png)
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Abstract
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Because AlexNet is too shallow to form a strong feature representation, the trackers based on the Siamese network have an accuracy gap comparing with state-of-the-art algorithms. Both deep features and appearance features benefit tracking accuracy. To combine these two kinds features, the modified pre-trained VGG16 network is fine-tuned as one branch of the backbone network. Secondly, an AlexNet branch is attached after the third convolutional layer of VGG16. Thus the response maps from both branches are merged to form a preliminary strong feature representation with deep features and shallow appearance features. Thirdly, a new mean Peak-to-side ratio(mPSR) loss is designed to help network learn target features adaptively. A channel attention block and the Average-Peak-to-Correlation Energy(APCE) are designed to help select contributed features and suppress distractors. SiamPF only takes ILSVRC2015-VID as training dataset, but it achieves excellent performance on OTB-2013 / OTB-2015 / VOT2015 / VOT2016 / VOT2017 while maintaining the real-time performance of 41FPS on the GTX 1080Ti.
Keywords:
Siamese network
Feature representation
mPSR
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