تقرير
SDTracker: Synthetic Data Based Multi-Object Tracking
| العنوان: | SDTracker: Synthetic Data Based Multi-Object Tracking |
|---|---|
| المؤلفون: | Guan, Yingda, Feng, Zhengyang, Chang, Huiying, Du, Kuo, Li, Tingting, Wang, Min |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | We present SDTracker, a method that harnesses the potential of synthetic data for multi-object tracking of real-world scenes in a domain generalization and semi-supervised fashion. First, we use the ImageNet dataset as an auxiliary to randomize the style of synthetic data. With out-of-domain data, we further enforce pyramid consistency loss across different "stylized" images from the same sample to learn domain invariant features. Second, we adopt the pseudo-labeling method to effectively utilize the unlabeled MOT17 training data. To obtain high-quality pseudo-labels, we apply proximal policy optimization (PPO2) algorithm to search confidence thresholds for each sequence. When using the unlabeled MOT17 training set, combined with the pure-motion tracking strategy upgraded via developed post-processing, we finally reach 61.4 HOTA. Comment: cvpr2022 workshop |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2303.14653 |
| رقم الأكسشن: | edsarx.2303.14653 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |