RPT: Learning Point Set Representation for Siamese Visual Tracking[ECCVW2020]
☀️ Currently, this code only supports the offline version of RPT, the absolute RPT code will come soon!
News
🏆 We are the Winner of VOT-2020 Short-Term challenge🏆 We get both 1st on the public and sequestered benchmark dataset of the VOT2020 Short-Term challenge☀️ ☀️ Our VOT2020-ST Winner presentation has been uploaded
Spotlight video
Models
| Dataset | pattern | A | R | EAO | Config. Filename |
|---|---|---|---|---|---|
| VOT2018 | offline | 0.610 | 0.150 | 0.497 | config_vot2018_offline.yaml |
| VOT2019 | offline | 0.598 | 0.261 | 0.409 | config_vot2019_offline.yaml |
| VOT2018 | online | 0.629 | 0.103 | 0.510 | |
| VOT2019 | online | 0.623 | 0.186 | 0.417 |
- The pretrained model can be downloaded from [google] or [baidu], extraction code: g4ac.
- The raw results can be downloaded [here], extraction code: mkbh.
Abstract
While remarkable progress has been made in robust visual tracking, accurate target state estimation still remains a highly challenging problem. In this paper, we argue that this issue is closely related to the prevalent bounding box representation, which provides only a coarse spatial extent of object. Thus an effcient visual tracking framework is proposed to accurately estimate the target state with a finer representation as a set of representative points. The point set is trained to indicate the semantically and geometrically significant positions of target region, enabling more fine-grained localization and modeling of object appearance. We further propose a multi-level aggregation strategy to obtain detailed structure information by fusing hierarchical convolution layers. Extensive experiments on several challenging benchmarks including OTB2015, VOT2018, VOT2019 and GOT-10k demonstrate that our method achieves new state-of-the-art performance while running at over 20 FPS.
Installation
Please find installation instructions in INSTALL.md
Quick Start: Using siamreppoints
Download pretrained models and put the siamreppoints.model in the correct directory in experiments
cd siamreppoints/tools
python test.py \
--snapshot ./snapshot/siamreppoints.model \ #model path
--dataset VOT2018 \ #dataset name
--config ./experiments/siamreppoints/config_vot2018_offline.yaml #config file cd siamreppoints/tools
python eval.py \
--tracker_path ./results \ #result path
--dataset VOT2018 \ #dataset name
--tracker_prefix 'siam' \ # tracker_name
--num 1 # number thread to eval