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Improved frame-by-frame object pose tracking in complex environments
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DOI：10.1007/s11042-018-5736-8.png)
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摘要
En 中文
Pose tracking is an important task in Augmented Reality (AR), interactive systems, and robotic systems. The frame-by-frame pose tracking that is effective in many cases still faces challenges in complex environments such as occlusions, illumination changes and flipping. In this paper, based on the optimization model offered by Ye et al. J Vis Commun Image Represent 44:72-81 (2017), three improvements are further proposed. First, a feature adjustment strategy based on a group of neighbors is offered to alleviate a sharp reduction of features. Then, when the features are no longer well representing the scene of interest, a score model based on a weighted histogram for result evaluations is presented to realize an adaptive interval. Besides, a forward-backward algorithm is provided to improve the accuracy by replacing the detection method with the tracking method. Experimental results manifest the effectiveness of the proposed algorithms.
Keyword:
Object detection
Frame-by-frame tracking
Pose estimation
Pose tracking
Complex environments
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期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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