Abstract
The strap-down missile-borne image guidance system can be easily affected by the unwanted jitters of the motion of the camera, and the subsequent recognition and tracking functions are also influenced, thus severely affecting the navigation accuracy of the image guidance system. So, a real-time image stabilization technology is needed to help improve the image quality of the image guidance system. To satisfy the real-time and accuracy requirements of image stabilization in the strap-down missile-borne image guidance system, an image stabilization method based on optical flow and image matching with binary feature descriptors is proposed. The global motion of consecutive frames is estimated by the pyramid Lucas-Kanade (LK) optical flow algorithm, and the interval frames image matching based on fast retina keypoint (FREAK) algorithm is used to reduce the cumulative trajectory error. A Kalman filter is designed to smooth the trajectory, which is conducive to fitting to the main motion of the guidance system. Simulations have been carried out, and the results show that the proposed algorithm improves the accuracy and real-time performance simultaneously compared to the state-of-art algorithms.
Highlights
The strap-down missile-borne image guidance system is a sort of real-time guidance system based on computer vision, which has been applied in practice, such as the image-guided miniature ammunition, Spike [1]
Lim et al [24] proposed an algorithm to tackle the problem of real-time video stabilization for unmanned aerial vehicles (UAVs), where they designed a suitable model for the global motion of UAV and employed the optical flow tracking
The accuracy of the three-level pyramid LK optical flow algorithm and image matching based on the fast retina keypoint (FREAK) descriptors are tested and compared using an image sequence where jitters are manually added as well
Summary
The strap-down missile-borne image guidance system is a sort of real-time guidance system based on computer vision, which has been applied in practice, such as the image-guided miniature ammunition, Spike [1]. Other point feature matching algorithms such as the binary robust independent elementary features (BRIEF) [20], binary robust invariant scalable keypoints (BRISK) [21], and fast retina keypoint (FREAK) [22] algorithms are proposed for faster computational speed while trying to retain the robustness to noise, scale invariance, and rotation invariance properties as much as possible These algorithms are hard to achieve video stabilization independently due to the limitation of their computational cost or accuracy. Lim et al [24] proposed an algorithm to tackle the problem of real-time video stabilization for unmanned aerial vehicles (UAVs), where they designed a suitable model for the global motion of UAV and employed the optical flow tracking. A real-time image stabilization algorithm for missile-borne strap-down image guidance system is proposed to distinguish the low-frequency movements, and remove the high-frequency jitters of the video. Experiments have been carried out to verify the feasibility and effectiveness of the proposed proposed algorithm
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