Abstract
Facial feature tracking is essential in imaging ballistocardiography for accurate heart rate estimation and enables motor degradation quantification in Parkinson's disease through skin feature tracking. While deep convolutional neural networks have shown remarkable accuracy in tracking tasks, they typically require extensive labeled data for supervised training. Our proposed pipeline employs a convolutional stacked autoencoder to match image crops with a reference crop containing the target feature, learning deep feature encodings specific to the object category in an unsupervised manner, thus reducing data requirements. To overcome edge effects making the performance dependent on crop size, we introduced a Gaussian weight on the residual errors of the pixels when calculating the loss function. Training the autoencoder on facial images and validating its performance on manually labeled face and hand videos, our Deep Feature Encodings (DFE) method demonstrated superior tracking accuracy with a mean error ranging from 0.6 to 3.3 pixels, outperforming traditional methods like SIFT, SURF, Lucas Kanade, and the latest transformers like PIPs++ and CoTracker. Overall, our unsupervised learning approach excels in tracking various skin features under significant motion conditions, providing superior feature descriptors for tracking, matching, and image registration compared to both traditional and state-of-the-art supervised learning methods.
Abstract (translated)
面部特征跟踪在球面心电图成像中至关重要，因为它能准确估计心脏率，并且通过皮肤特征跟踪在帕金森病患者中实现运动降解量化。虽然深度卷积神经网络在跟踪任务中表现出惊人的准确性，但通常需要大量的有标签数据进行监督训练。我们提出的方案采用卷积堆叠自编码器将图像块与包含目标特征的参考块匹配，无监督地学习特定于物体类别的深度特征编码，从而减少了数据需求。为了克服边缘效果，使性能取决于图像大小，我们在计算损失函数时对像素残差应用高斯权重。在面部图像上训练自编码器并验证其性能，我们的Deep Feature Encodings（DFE）方法在平均误差范围内从0.6到3.3像素，超越了传统方法（如SIFT，SURF，Lucas Kanade）和最先进的变压器（如PIPs++和CoTracker），展示了卓越的跟踪精度。总的来说，我们的无监督学习方法在重大运动条件下 excels于跟踪各种皮肤特征，为跟踪、匹配和图像配准提供卓越的性能，与传统和最先进的监督学习方法相比。
URL
https://arxiv.org/abs/2405.04943
