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.

本文提出SDTracker，一种利用合成数据进行多目标跟踪的方法，实现领域泛化和半监督学习。通过对图像进行风格随机化，使用ImageNet数据集获得合成数据，通过对不同“风格”图像进行金字塔一致性损失的强制执行来学习领域不变特征；运用伪标记方法，使用PPO2算法搜索每个序列的置信度阈值，以获得高质量的伪标记，进而在MOT17训练集上实现多目标跟踪并获得61.4的HOTA值

SDTracker：基于合成数据的多目标跟踪