Purpose

Visual tracking technology enables industrial robots interacting with human beings intelligently. However, due to the complexity of the tracking problem, the accuracy of visual target tracking still has great space for improvement. This paper aims to propose an accurate visual target tracking method based on standard hedging and feature fusion.

Design/methodology/approach

For this study, the authors first learn the discriminative information between targets and similar objects in the histogram of oriented gradients by feature optimization method, and then use standard hedging algorithms to dynamically balance the weights between different feature optimization components. Moreover, they penalize the filter coefficients by incorporating spatial regularization coefficient and extend the Kernelized Correlation Filter for robust tracking. Finally, a model update mechanism to improve the effectiveness of the tracking is proposed.

Findings

Extensive experimental results demonstrate the superior performance of the proposed method comparing to the state-of-the-art tracking methods.

Originality/value

Improvements to existing visual target tracking algorithms are achieved through feature fusion and standard hedging algorithms to further improve the tracking accuracy of robots on targets in reality.

中文翻译：

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基于标准对冲与特征融合的机器人目标跟踪

目的

视觉跟踪技术使工业机器人与人类进行智能交互。然而，由于跟踪问题的复杂性，视觉目标跟踪的精度仍有很大的提升空间。本文旨在提出一种基于标准对冲和特征融合的精确视觉目标跟踪方法。

设计/方法/方法

对于这项研究，作者首先通过特征优化方法在定向梯度直方图中学习目标和相似对象之间的判别信息，然后使用标准对冲算法来动态平衡不同特征优化组件之间的权重。此外，他们通过合并空间正则化系数来惩罚滤波器系数，并扩展核相关滤波器以实现稳健跟踪。最后，提出了一种模型更新机制来提高跟踪的有效性。

发现

大量的实验结果表明，与最先进的跟踪方法相比，所提出的方法具有优越的性能。

原创性/价值

通过特征融合和标准对冲算法对现有视觉目标跟踪算法进行改进，进一步提高机器人对现实目标的跟踪精度。