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MANet

RGBT234 dataset

链接:https://pan.baidu.com/s/1weaiBh0_yH2BQni5eTxHgg 提取码:qvsq

RGBT210 dataset

链接:https://pan.baidu.com/s/1FClmX0SH3WarcczkEQbmwA 提取码:ps8j

GTOT dataset

链接:https://pan.baidu.com/s/1zaR6aXh9PVQs063Q_b9zQg 提取码:ajma

RGBT234 toolkit

链接:https://pan.baidu.com/s/1UksOGtD2yl6k8mtB-Wr39A 提取码:4f68

RGBT210 toolkit

链接:https://pan.baidu.com/s/1KHMlbhu5R29CJvundGL4Sw 提取码:8wtc

GTOT toolkit

链接:https://pan.baidu.com/s/1iVVAXS4LZLvoQSGQnz7ROw 提取码:d53m

MANet result

Here, we only upload the result file of paper (PR_0.777 SR_0.539 on RGBT234, PR_0.894 SR_0.724 on GTOT.)

This code is update version based on submitted for VOT2019-RGBT challenge code simplified version.

So there are some differences from MANet's paper.

Prerequisites

CPU: Intel(R) Core(TM) i7-7700K CPU @ 3.75GHz GPU: NVIDIA GTX1080(8GB) Ubuntu 16.04

  • python2.7
  • pytorch == 0.3.1
  • numpy
  • PIL
  • some others library functions

Pretrained model for MANet

In our tracker, we use MDNet as our backbone and extend to multi-modal tracker.

We use imagenet-vgg-m.mat as our pretrain model.

Train

You can choose two stage train or end2end train

two stage train:

  • Stage1. use RGBT dataset to train all network, and then save finally model;
  • Stage2. you only need to load the parameters of GA from the stage1 saved model, and use same RGBT dataset to train the MA and IA while fix GA.

end2end train:

  • Here train method is same with MDNet

Pretrain model :https://drive.google.com/open?id=1aO6LhOTxmpd7o_JXPLPjL3LsrQ5oqbl7

Run tracker

In the tracking/run_tracker.py file, you need to change dataset path and save result file dirpath In the tracking/options.py file, you need to set model file path and set learning rate depend on annotation. In tracking and train stages, you need to update modules/MANet3x1x1_IC.py file depend on annotation.

Tracking model:https://drive.google.com/open?id=1Png508G4kQPI6HNewKQ4cfS36CvoSFSN

Result

image

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Multi-Adapter RGBT Tracking implementation on Pytorch

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