Deng Junli, Yao Haoyuan, Shi Ping
School of Information and Communication Engineering, Communication University of China, Beijing 100024, China.
Sensors (Basel). 2023 Oct 12;23(20):8406. doi: 10.3390/s23208406.
Data-driven pose estimation methods often assume equal distributions between training and test data. However, in reality, this assumption does not always hold true, leading to significant performance degradation due to distribution mismatches. In this study, our objective is to enhance the cross-domain robustness of multi-view, multi-person 3D pose estimation. We tackle the domain shift challenge through three key approaches: (1) A domain adaptation component is introduced to improve estimation accuracy for specific target domains. (2) By incorporating a dropout mechanism, we train a more reliable model tailored to the target domain. (3) Transferable Parameter Learning is employed to retain crucial parameters for learning domain-invariant data. The foundation for these approaches lies in the H-divergence theory and the lottery ticket hypothesis, which are realized through adversarial training by learning domain classifiers. Our proposed methodology is evaluated using three datasets: Panoptic, Shelf, and Campus, allowing us to assess its efficacy in addressing domain shifts in multi-view, multi-person pose estimation. Both qualitative and quantitative experiments demonstrate that our algorithm performs well in two different domain shift scenarios.
数据驱动的姿态估计方法通常假设训练数据和测试数据之间具有均匀分布。然而，在现实中，这一假设并不总是成立，由于分布不匹配会导致显著的性能下降。在本研究中，我们的目标是提高多视图、多人3D姿态估计的跨域鲁棒性。我们通过三种关键方法应对域转移挑战：（1）引入一个域适应组件，以提高针对特定目标域的估计精度。（2）通过纳入随机失活机制，我们训练一个更适合目标域的可靠模型。（3）采用可转移参数学习来保留用于学习域不变数据的关键参数。这些方法的基础是H散度理论和彩票假设，它们通过学习域分类器的对抗训练来实现。我们提出的方法使用三个数据集进行评估：全景、货架和校园，从而使我们能够评估其在解决多视图、多人姿态估计中的域转移问题方面的有效性。定性和定量实验均表明，我们的算法在两种不同的域转移场景中表现良好。
