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ROAM: Recurrently Optimizing Tracking Model

Introduction

This is the PyTorch implementation of our ROAM tracker published in CVPR, 2020. Detailed comparision results can be found in the author's webpage

Prerequisites

  • Python 3.5 or higher
  • PyTorch 1.4.0 or higher

Path setting

Set proper root_dir in config.py accordingly in order to proceed the following step. Make sure that you place the tracking data properly according to your path setting.

Training

  1. Download the ILSRVC data from the official website and extract it to proper place according to the path in config.py. Pretrained vgg-16.mat file can be download from here
  2. Then run the python3 make_vid_info.py in to build the meta data file for ILSVRC data.
  3. Run:
python3 experiment.py \
  --mGPUs \
  --epochs 20 \
  --bs [BATCH_SIZE] \
  --nw [NUM_WORKERS] \
  --lr_mi 1e-6 \
  --lr_mo 1e-3

to train the model. Note we train our model on a 4-GPUs machine with BATCH_SIZE=16

Tracking Demo

After training, you can run python3 demo.py to test our tracker.

Citing ROAM

If you find the code is helpful, please cite

@inproceedings{Yang2020cvpr,
	author = {Yang, Tianyu and Xu, Pengfei and Hu, Runbo and Chai, Hua and Chan, Antoni B},
	booktitle = {CVPR},
	title = {{ROAM: Recurrently Optimizing Tracking Model}},
	year = {2020}
}

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