Chae, Seungho Hong, Je Hyeong Choi, Heeseung Kim, Ig-Jae 2024-01-19T17:01:40Z 2024-01-19T17:01:40Z 2021-09-05 2020-08 2169-3536 https://pubs.kist.re.kr/handle/201004/118290 Accurate pose estimation of planar objects is a key computation in visual localization tasks, with recent studies showing remarkable progress on a handful of baseline datasets. Nonetheless, achieving similar performance on sequences in unconstrained environments is still an ongoing quest to be accomplished, largely due to the existence of several sources of errors, which are correlated but often only partly tackled in the literature. In this article, we propose POP, a generic real-time planar-object pose-estimation framework which is designed to handle the aforementioned types of errors while not losing generality to a specific choice of keypoint detection or tracking algorithm. The essence of POP lies in activating keypoint detection module in the background as well as adding several refinement steps in order to reduce correlated sources of errors within the pipeline. We provide extensive experimental evaluations against state-of-the-art planar object tracking algorithms on baseline and more challenging datasets, empirically demonstrating the effectiveness of the POP framework for scenes with large environmental variations. English IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC TRACKING POP: A Generic Framework for Real-Time Pose Estimation of Planar Objects Article 10.1109/ACCESS.2020.3020309 1 IEEE ACCESS, v.8, pp.164065 - 164076 IEEE ACCESS 8 164065 164076 scie scopus 000572933200001 2-s2.0-85102809126 Computer Science, Information Systems Engineering, Electrical & Electronic Telecommunications Computer Science Engineering Telecommunications Article TRACKING Feature extraction Pose estimation Pipelines Object tracking Real-time systems Robustness Planar object tracking pose estimation keypoint matching structured output SVMs