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Improved Kernel Correlation Filter Based Moving Target Tracking for Robot Grasping
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AI
DOI：10.1109/TIM.2022.3195258.png)
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摘要
En 中文
Tracking and grasping moving objects are hot topics in the field of robots, which provides great potential in the industrial scene and human-computer cooperation. Based on the kernel correlation filter and vision 3-D reconstruction, this article proposes a visual-based tracking and grasping method for moving targets. An improved algorithm based on the kernel correlation filter is proposed for object tracking. A scale pool is constructed, and a scale filter is trained to solve the problem of algorithm scale adaptation. At the same time, the judgment mechanism of tracking results, secondary detection, and modification update mechanisms are added to improve the robustness of the algorithm. Combined with an RGB-D camera, the target is reconstructed to obtain the 3-D pose of the target. The tracking and intercepting strategy is adopted to grasp the moving target. The proposed method is proven to have good performance through dataset comparison tests and experiments on real robot systems.
Keyword:
Target tracking
Filtering algorithms
Grasping
Robots
Kernel
Correlation
Training
3-D reconstruction
kernelized correlation filters
scale adaptation
secondary detection
tracking and grasping
期刊
IF:
5.9
论文数:
1.9W
被引数:
5.8W
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引用论文
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IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
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