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SiamVGG: Visual Tracking using Deeper Siamese Networks
Recently, we have seen a rapid development of Deep Neural Network (DNN) based
visual tracking solutions. Some trackers combine the DNN-based solutions with
Discriminative Correlation Filters (DCF) to extract semantic features and
successfully deliver the state-of-the-art tracking accuracy. However, these
solutions are highly compute-intensive, which require long processing time,
resulting unsecured real-time performance. To deliver both high accuracy and
reliable real-time performance, we propose a novel tracker called SiamVGG. It
combines a Convolutional Neural Network (CNN) backbone and a cross-correlation
operator, and takes advantage of the features from exemplary images for more
accurate object tracking.
The architecture of SiamVGG is customized from VGG-16, with the parameters
shared by both exemplary images and desired input video frames.
We demonstrate the proposed SiamVGG on OTB-2013/50/100 and VOT 2015/2016/2017
datasets with the state-of-the-art accuracy while maintaining a decent
real-time performance of 50 FPS running on a GTX 1080Ti. Our design can achieve
2% higher Expected Average Overlap (EAO) compared to the ECO and C-COT in
VOT2017 Challenge
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