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Visual Tracking by TridenAlign and Context Embedding

Visual Tracking by TridentAlign and Context Embedding (TACT)

Test code for "Visual Tracking by TridentAlign and Context Embedding"

Janghoon Choi, Junseok Kwon, and Kyoung Mu Lee

arXiv paper

Overall Framework

Results on LaSOT test set

• Link to LaSOT dataset
• Raw results available on Google drive

Dependencies

• Ubuntu 18.04
• Python==2.7.17
• numpy==1.16.5
• pytorch==1.3.0
• matplotlib==2.2.4
• opencv==4.1.0.25
• moviepy==1.0.0
• tqdm==4.32.1

Usage

Prerequisites

• Download network weights from Google drive
• Copy network weight files ckpt_res18.tar and ckpt_res50.tar to ckpt/ folder
• Choose between TACT-18 and TACT-50 by modifying the cfgs/cfg_test.py file (default: TACT-50)

To test tracker on LaSOT test set

• Download LaSOT dataset from link
• Modify cfgs/cfg_test.py file to local LaSOTBenchmark folder path
• Run python test_tracker.py

To test tracker on an arbitrary sequence

• Using run_track_seq() function in tracker_batch.py, tracker can run on an arbitrary sequence
• Provide the function with following variables
  • seq_name : name of the given sequence
  • seq_path : path to the given sequence
  • seq_imlist : list of image file names of the given sequence
  • seq_gt : ground truth box annotations of the given sequence (may only contain annotation for initial frame, [x_min,y_min,width,height] format)

Raw results on other datasets

• Link to raw results on Google drive
  • Results for test sets of LaSOT, OxUvA, GOT-10k, TrackingNet

Citation

If you find our work useful for your research, please consider citing the following paper:

@article{choi2020tact,
  title={Visual tracking by tridentalign and context embedding},
  author={Choi, Janghoon and Kwon, Junseok and Lee, Kyoung Mu},
  journal={arXiv preprint arXiv:2007.06887},
  year={2020}
}