Multiple-criteria-Based Object Pose Tracking in RGB Videos

Majcher, Mateusz; Kwolek, Bogdan

doi:10.1007/978-3-031-16014-1_38

Cited by 1 publication

(1 citation statement)

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“…Using both keypoints and segmentation in object pose tracking with the support of a multi-criteria decision algorithm can help overcome the limitations that arise when only one of the mentioned object representations is used or when both object representations are combined, as in the above-mentioned work [25]. In a method proposed in [18] a multi-criteria analysis is leveraged to enhance 6D object pose tracking in RGB videos on the basis of object keypoints and object shape.…”

Section: Introductionmentioning

confidence: 99%

TOPSIS Aided Object Pose Tracking on RGB Images

Majcher,

Kwolek

2023

IEEE Access

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In the problem of object pose estimation, one way to cope with the effect of ambiguity is to use multiple hypotheses. In this work, rather than generating the output pose based on a single object pose, our objective is to enable the system to be aware of the potential object ambiguity through maintaining multiple pose hypotheses. Firstly, we propose a pipeline for 6D object pose tracking on RGB images, wherein a key design is a fuzzy TOPSIS module that takes full advantage of multi-criteria decision making under uncertainties. Secondly, using decision variables determined on features that are frequently utilized in object pose estimation or tracking like segmented masks, fiducial keypoints, and distance transform the proposed method permits achieving tangible performance gains. An hourglass-based neural network is proposed to jointly detect object keypoints, predict the object's non-occluded part, and to predict the object's occluded part. To verify our designs, we conducted thorough experiments on the YCB-Video benchmark dataset. Besides, our method achieves competitive results in terms of ADD scores on the YCB-Video, showing that maintaining multiple pose hypotheses is beneficial to the task of object pose tracking. We observe that our method achieves competitive results against six recent methods estimating object pose from single frame and two SOTA object pose trackers. Extensive ablation studies verify our design choices.INDEX TERMS Object pose tracking, multi-criteria decision making, multi-task neural networks, handling uncertain data, fuzzy TOPSIS.

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Section: Introductionmentioning

confidence: 99%