Election Based Pose Estimation of Moving Objects

Liming Gao, Chongwen Wang

Abstract: In this work, a key-points based method is presented to track and estimate the pose of rigid objects, which is achieved by using the tracked points of the object to calculate the attitude changes [1]. We propose to select a few points to represent the posture of the object and maintain efficiency. A standard feature point tracking algorithm is applied to detect and match feature points. The presented method is able to overcome key-points’ errors as well as decrease the computational complexity. In order to reduce the error caused by feature points detection, we use the tacked key-points and their relation with the target center to get the most reliable tracking result. To avoid introducing errors, the model will maintain the features generated in initialization. Finally, the most reliable candidates will be picked out to calculate the pose information, and the small amount of key-points with highly accuracy can ensure real-time performance.