Target tracking based on standard hedging and feature fusion for robot

Abstract

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.

Guest editorial

“…Chan et al propose to track targets based on standard hedging and feature fusion for robot (Chan et al, 2021). The proposed method achieves better accuracy of visual target tracking even in complex backgrounds, by efficiently learning the discriminative information between targets and similar objects, then using standard hedging algorithms to dynamically balance the weights between different feature optimization components.…”