论文标题
Pietrack：基于合成数据训练和自我监管的域适应性的MOT解决方案
PieTrack: An MOT solution based on synthetic data training and self-supervised domain adaptation
论文作者
论文摘要
为了应付对人类检测的标签数据和隐私问题的不断增长的需求，合成数据已被用作替代品，并在人类检测和跟踪任务中显示出令人鼓舞的结果。我们参加了关于基准测试多目标跟踪（BMTT）的第七届研讨会，主题是“合成数据可以带我们多远”？我们的解决方案Pietrack是根据合成数据开发的，而无需使用任何预训练的权重。我们提出了一种自我监管的域适应方法，该方法可以减轻合成（例如Motsynth）和真实数据（例如Mot17）之间的域移位问题，而无需涉及额外的人类标签。通过利用拟议的多尺度合奏推理，我们在MOT17测试集中达到了最终的HOTA得分为58.7，在挑战中排名第三。
In order to cope with the increasing demand for labeling data and privacy issues with human detection, synthetic data has been used as a substitute and showing promising results in human detection and tracking tasks. We participate in the 7th Workshop on Benchmarking Multi-Target Tracking (BMTT), themed on "How Far Can Synthetic Data Take us"? Our solution, PieTrack, is developed based on synthetic data without using any pre-trained weights. We propose a self-supervised domain adaptation method that enables mitigating the domain shift issue between the synthetic (e.g., MOTSynth) and real data (e.g., MOT17) without involving extra human labels. By leveraging the proposed multi-scale ensemble inference, we achieved a final HOTA score of 58.7 on the MOT17 testing set, ranked third place in the challenge.
