SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks

Bo Li *,   Wei Wu *,   Qiang Wang *,   Fangyi Zhang,   Junliang Xing,   Junjie Yan  

  SenseTime Research   CASIA   ICT

Internship opening: Looking for interns to work at SenseTime Group Limited. e-mail

* means equal contribution

 
pipeline picture

 

Siamese network based trackers formulate tracking as convolutional feature cross-correlation between a target template and a search region. However, Siamese trackers still have an accuracy gap compared with state-of-the-art algorithms and they cannot take advantage of features from deep networks, such as ResNet-50 or deeper. In this work we prove the core reason comes from the lack of strict translation invariance. By comprehensive theoretical analysis and experimental validations, we break this restriction through a simple yet effective spatial aware sampling strategy and successfully train a ResNet-driven Siamese tracker with significant performance gain. Moreover, we propose a new model architecture to perform layer-wise and depth-wise aggregations, which not only further improves the accuracy but also reduces the model size. We conduct extensive ablation studies to demonstrate the effectiveness of the proposed tracker, which obtains currently the best results on five large tracking benchmarks, including OTB2015, VOT2018, UAV123, LaSOT, and TrackingNet. Our model will be released to facilitate further researches.

 
 

Paper (arXiv) Now accepted by CVPR 2019

▸ Results  

[VOT2018] [VOT2016] [VOT18-LT] [OTB2015] [UAV123] [LaSOT] [TrackingNet]

bibtex

@article{li2018siamrpn++,
  Author    = {Li, Bo and Wu, Wei and Wang, Qiang and Zhang, Fangyi and Xing, Junliang and Yan, Junjie},
  Title     = {SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks},
  Journal   = {arXiv preprint arXiv:1812.11703},
  Year      = {2018}
}