论文标题
组合3D模型轮廓能量和对象跟踪的关键点
Combining 3D Model Contour Energy and Keypoints for Object Tracking
论文作者
论文摘要
我们提出了一种基于单眼模型的3D跟踪的新组合方法。通过使用基于按键的技术来估计初步对象姿势。然后通过优化轮廓能函数来完善姿势。能量决定了模型投影轮廓与图像边缘之间的对应关系。它是根据原始图像梯度的强度和方向计算得出的。为了优化，我们提出了一种技术和搜索区域约束，允许克服本地Optima并考虑通过基于Kepoint的姿势估计获得的信息。由于其结合性质，我们的方法消除了基于按键和基于边缘的方法的众多问题。我们通过将其与公共基准数据集上的最新方法进行比较，展示了我们的方法的效率，该方法包括具有各种照明条件，运动模式和速度的视频。
We present a new combined approach for monocular model-based 3D tracking. A preliminary object pose is estimated by using a keypoint-based technique. The pose is then refined by optimizing the contour energy function. The energy determines the degree of correspondence between the contour of the model projection and the image edges. It is calculated based on both the intensity and orientation of the raw image gradient. For optimization, we propose a technique and search area constraints that allow overcoming the local optima and taking into account information obtained through keypoint-based pose estimation. Owing to its combined nature, our method eliminates numerous issues of keypoint-based and edge-based approaches. We demonstrate the efficiency of our method by comparing it with state-of-the-art methods on a public benchmark dataset that includes videos with various lighting conditions, movement patterns, and speed.
