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The video image anti shake algorithm is a dynamic video correction method based on a six axis IMU (Inertial Measurement Unit), aimed at effectively reducing the impact of external shaking on the video and significantly improving the stability of the video in various vibration scenarios. In scenarios such as intelligent vehicles, this algorithm can prevent high-frequency and small vibrations from causing target blurring, thereby affecting the accuracy of target detection and tracking.
Generally speaking, anti shake algorithms are mainly divided into two steps: motion estimation and motion compensation.
Motion estimation: The goal of this section is to find the optimal motion vector between video frames. This can be done by calculating the difference between the front and back frames to find the minimum distortion. Common methods include grayscale projection, block matching, bitplane matching, edge matching, and feature point matching.
Motion compensation: Compensates the current frame based on the estimated motion vector to remove jitter. This can be achieved by establishing an affine transformation model, estimating the relative motion of video frames with parameters, and then correcting the jittered frames to the reference frame.
In terms of specific implementation, the anti shake algorithm also needs to be combined with other technologies, such as inter frame denoising and public buffer, to improve the efficiency and accuracy of the algorithm. At the same time, the anti shake algorithm may require specific optimization and adjustment for different scenarios and devices.
The video image anti shake algorithm is a dynamic video correction method based on a six axis IMU (Inertial Measurement Unit), aimed at effectively reducing the impact of external shaking on the video and significantly improving the stability of the video in various vibration scenarios. In scenarios such as intelligent vehicles, this algorithm can prevent high-frequency and small vibrations from causing target blurring, thereby affecting the accuracy of target detection and tracking.
Generally speaking, anti shake algorithms are mainly divided into two steps: motion estimation and motion compensation.
Motion estimation: The goal of this section is to find the optimal motion vector between video frames. This can be done by calculating the difference between the front and back frames to find the minimum distortion. Common methods include grayscale projection, block matching, bitplane matching, edge matching, and feature point matching.
Motion compensation: Compensates the current frame based on the estimated motion vector to remove jitter. This can be achieved by establishing an affine transformation model, estimating the relative motion of video frames with parameters, and then correcting the jittered frames to the reference frame.
In terms of specific implementation, the anti shake algorithm also needs to be combined with other technologies, such as inter frame denoising and public buffer, to improve the efficiency and accuracy of the algorithm. At the same time, the anti shake algorithm may require specific optimization and adjustment for different scenarios and devices.
