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Zhongdao / UniTrack

[NeurIPS'21] Unified tracking framework with a single appearance model. It supports Single Object Tracking (SOT), Video Object Segmentation (VOS), Multi-Object Tracking (MOT), Multi-Object Tracking and Segmentation (MOTS), Pose Tracking, Video Instance Segmentation (VIS), and class-agnostic MOT (e.g. TAO dataset).

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SOT on LaSOT

Flowerfan opened this issue · comments

commented

Hi, Zhongdao,

Thank you for your great work!

I have tested your code on LaSOT using the crw_resnet18_s3 model by modifying the datasets root in utils.py.
But the AUC is only 23.02 on this dataset. I'm not sure I got the correct results. Have you tested the unitrack on LaSOT or GOT10k? Could you provide the results on LaSOT and GOT10k at your convenience?

commented

Hi @Flowerfan,
We indeed have results on more SOT datasets (we did these during rebuttal). You will find these results in the camera-ready version of our supplementary material, and below:
image

Here UniTrack uses an ImageNet pre-trained ResNet-50 as the appearance model.
Your results (23.02 on LaSOT) seem correct. If you want to obtain a better result I suggest slightly tuning hyperparameters here:

UniTrack/test/test_sot_cfnet.py

Line 44 in 44ae779

class TrackerConfig(object):

For instance, change this line

UniTrack/test/test_sot_cfnet.py

Line 56 in 44ae779

num_scale = 3

to num_scale = 5 helps.

commented

Thank you for sharing these results! Much appreciate!