Video object tracking method based on feature optical flow and online ensemble learning
An integrated learning and target tracking technology, applied in image data processing, instrumentation, computing, etc., can solve problems such as poor tracking results
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[0041] The concrete steps of the inventive method are as follows:
[0042] 1. Tracking part.
[0043] The preprocessing part inputs the video sequence, uses the function that comes with OpenCV to track the feature points of each frame with the iterative pyramid optical flow method, and obtains the position of these feature points in the next frame.
[0044] For target tracking without scaling, proceed as follows:
[0045] (i) When the number of feature points is less than 4, use the median value of the displacement of these feature points in the x direction and y direction as the displacement of the entire target in the x direction and y direction.
[0046] (ii) When the number of feature points is ≥ 4, use the RANSAC algorithm to calculate the transformation matrix from the bounding box of the previous frame to the bounding box of the next frame.
[0047] Since the moving object may have slight scaling in two consecutive frames, it is incomplete to only consider the unscale...
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