# Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms
## 1 Introduction
Recently, [PERSON] extended the conventional HD method to a factorization-based HD method [14] to deal with multi-plan multi-view situations robustly against occlusion. The authors in [15] extended the conventional HD method to resolve the shape-from-texture issue, which is also a well-known problem in computer vision and is equivalent to the problem of multi-plane-based camera pose estimation. In [16], [PERSON] et al. proposed an HD-based planar-surface pose tracking algorithm that can work in environments containing multiple planes. They showed that the proposed planar-surface pose tracker is a simplified general-motion tracker, but is more reliable and faster in an environment with a visible planar surface. To improve the real-time performance of this tracker, [PERSON] and [PERSON] combined the tracker with [PERSON]'s Sum-of-Squared-Differences (SSD) template tracker [17] to speed up the pose tracking process [18]. However, the authors in [19] showed that the Jacobian Approximation (JA) used in [PERSON]'s SSD template tracker is less reliable for real images. Instead, they suggested training the template tracker using a Hyperplane Approximation (HA), which provides more reliable estimation results. To improve the robustness of template tracking, [PERSON] et al. proposed an online robust image alignment method via subspace learning from Image Gradient Orientations (IGO) [20], which provides more illumination- and occlusion-robust image alignment due to processing in the IGO domain. In [21], [PERSON] et al. proposed a robust monocular pose estimation method based on temporally-consistent local color histograms, which can be used as statistical object descriptors within a template matching strategy for pose recovery in a cluttered environment. It is also possible for deep learning methods to reach the same purpose. For instance, the authors in [22] combined the strengths of convolutional neural networks and the Lucas-Kanade algorithm to implement a cascaded Lucas-Kanade network architecture for image alignment. In [23], [PERSON] et al. proposed a robust direct homography tracking method, which integrates the extended Kalman filter to speed up the convergence and to increase the robustness of the tracking. In this study, a novel and efficient model-based object pose estimation algorithm is proposed. The proposed algorithm is inspired by the work presented in [18], but we employ multiple HA template trackers instead of the SSD tracker to track an OOI having a multi-planar structure. Based on the tracking result, the correspondences between image features and control points of a given 3D CAD model can be determined efficiently. Moreover, an enhanced 3D pose estimation algorithm is proposed by combining an HD-based initial pose solver and a Perspective-n-Point (PnP) solver to improve the accuracy of pose estimation results. Experimental results demonstrate the tracking performance and estimation accuracy of the proposed model-based object pose tracker. The rest of this paper is organized as follows. Section 2 introduces the system framework of the proposed model-based object pose estimation algorithm. Section 3 presents the proposed multi-template tracking design for the proposed algorithm to improve the performance of pose tracking with multiple templates. Section 4 describes the proposed model-based 3D pose estimation method based on the template tracking result. Experimental results are reported in Section 5 to evaluate the effectiveness and efficiency of the proposed model-based object pose estimation method. Section 6 concludes the contributions of this paper.