Abstract
The use of machine vision to track the receiver aircraft’s receptacle is one of the key steps in automated aerial refueling. Traditional Meanshift tracking algorithm’s kernel bandwidth is fixed, when the target size changes, it will produce with the bias even with the loss of the situation. Aiming at this problem, this paper proposes a kernel bandwidth adaptive algorithm based on corner matching. First, use backward tracking method to amend the core window center. Then according to the Harris algorithm to detect the corner, use small-scale approach for the two points between frames to match and regression analysis, namely determine the update parameters of nuclear window width. Experiments show that the algorithm can track the target accurately when the size of tracking target change, and the kernel window width can be better suited to the target size.
Highlights
IntroductionUAV (unmanned aerial vehicles) play an increasingly important role in the field of civilian and military fields because of its small size, low cost, easy to use, etc
UAV play an increasingly important role in the field of civilian and military fields because of its small size, low cost, easy to use, etc
The development of machine vision provides an effective support for the UAV autonomous air refueling
Summary
UAV (unmanned aerial vehicles) play an increasingly important role in the field of civilian and military fields because of its small size, low cost, easy to use, etc. Document [5] uses positive and negative 10% increments to change the kernel function bandwidth It needs three times of Meanshift tracking based on three kernel bandwidth in the tracking of each frame. It will get a good tracking effect when the target is smaller, not suitable for autonomous air refueling process by the receiver aircraft’s receptacle tracking. Document [6] changes the kernel bandwidth by the backtracking method based on the corner feature, but the tracking is not accurate enough only when the target feature is described Aiming at this problem, this paper proposes a kernel bandwidth adaptive algorithm based on corner matching. The weight of each pixel is calculated by the following kernel function:
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