The classical Mean shift algorithm for target tracking, when it is used to track window in a complex background, appears too vulnerable to avoid window jitter, which leads to a failure of accurately tracking the target; while in tracking the target of fast-moving video sequences, the loss of tracking targets can not be avoid. As an improvement based on the classical Mean shift algorithm, the proposed algorithm with characteristic of combining tracking differentiator (TD) can eliminate window jitter and predict the target location with the help of TD. Experiments demonstrate that the proposed algorithm is capable of getting over deficiency of the original algorithm and holding improved stability and robustness.
For electronic image stabilization process of the image sequence, the target scenes in different depth of image field have
different motion vectors. There is not a compensation amount can stable both close-range and long-range target scene.
This article from the optical imaging model analysis the reasons, aimed at greater depth of field distribution within the
scope of the video sequence, using Harris corner algorithm detection in different depth of field goal of characteristic
points, and feature matching, Calculation of different depth to the target motion vector validation previous theoretical
derivation. Proposed a motion vector compensation method based on electronic image stabilization image quality
assessment. Compensate the image after adjust the distribution of motion vector on the assessment feedback of the image
inter-frame differential map. Results show that the motion vector compensation effect in the optimization of the value is
better than the global motion vector compensation.
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