Real-Time Model-Based Rigid Object Pose Estimation and Tracking Combining Dense and Sparse Visual Cues

2013 ◽  
Author(s):  
Karl Pauwels ◽  
Leonardo Rubio ◽  
Javier Diaz ◽  
Eduardo Ros
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Visual Cues ◽  
Time Model ◽  
Rigid Object ◽  
Model Based ◽  
Object Pose Estimation

scholarly journals Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 122 ◽  
Author(s):  
Chi-Yi Tsai ◽  
Kuang-Jui Hsu ◽  
Humaira Nisar
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Image Features ◽  
Estimation Methods ◽  
Reference Image ◽  
Practical Applications ◽  
Pose Tracking ◽  
Model Based ◽  
Object Pose Estimation ◽  
Template Tracking

Three-Dimensional (3D) object pose estimation plays a crucial role in computer vision because it is an essential function in many practical applications. In this paper, we propose a real-time model-based object pose estimation algorithm, which integrates template matching and Perspective-n-Point (PnP) pose estimation methods to deal with this issue efficiently. The proposed method firstly extracts and matches keypoints of the scene image and the object reference image. Based on the matched keypoints, a two-dimensional (2D) planar transformation between the reference image and the detected object can be formulated by a homography matrix, which can initialize a template tracking algorithm efficiently. Based on the template tracking result, the correspondence between image features and control points of the Computer-Aided Design (CAD) model of the object can be determined efficiently, thus leading to a fast 3D pose tracking result. Finally, the 3D pose of the object with respect to the camera is estimated by a PnP solver based on the tracked 2D-3D correspondences, which improves the accuracy of the pose estimation. Experimental results show that the proposed method not only achieves real-time performance in tracking multiple objects, but also provides accurate pose estimation results. These advantages make the proposed method suitable for many practical applications, such as augmented reality.

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