论文标题
HVC-NET：平面对象跟踪的统一同构，可见性和信心学习
HVC-Net: Unifying Homography, Visibility, and Confidence Learning for Planar Object Tracking
论文作者
论文摘要
整个视频序列上的强大而准确的平面跟踪对于许多视觉应用至关重要。平面对象跟踪的关键是在参考图像和跟踪图像之间查找以同型模型建模的对象对应关系。现有方法倾向于获得错误的对应关系，外观变化，相机相对运动和遮挡。为了减轻这个问题，我们提出了一个统一的卷积神经网络（CNN）模型，该模型共同考虑了同谱，可见性和信心。首先，我们介绍了相关块，这些块明确地说明了本地外观变化，而相机对象相对运动是我们模型的基础。其次，我们共同学习将相机对象相对运动与遮挡联系起来的同构和可见性。第三，我们提出了一个置信模块，该模块积极监视从相关块中获得的像素相关分布中的估计质量。所有这些模块都插入了Lucas-Kanade（LK）跟踪管道中，以获得准确且健壮的平面对象跟踪。我们的方法表现优于公共锅和TMT数据集上的最新方法。在现实世界中，还可以验证其出色的性能，从而综合了高质量的视频内广告。
Robust and accurate planar tracking over a whole video sequence is vitally important for many vision applications. The key to planar object tracking is to find object correspondences, modeled by homography, between the reference image and the tracked image. Existing methods tend to obtain wrong correspondences with changing appearance variations, camera-object relative motions and occlusions. To alleviate this problem, we present a unified convolutional neural network (CNN) model that jointly considers homography, visibility, and confidence. First, we introduce correlation blocks that explicitly account for the local appearance changes and camera-object relative motions as the base of our model. Second, we jointly learn the homography and visibility that links camera-object relative motions with occlusions. Third, we propose a confidence module that actively monitors the estimation quality from the pixel correlation distributions obtained in correlation blocks. All these modules are plugged into a Lucas-Kanade (LK) tracking pipeline to obtain both accurate and robust planar object tracking. Our approach outperforms the state-of-the-art methods on public POT and TMT datasets. Its superior performance is also verified on a real-world application, synthesizing high-quality in-video advertisements.
