Motion Vector Detection Using Block Matching Algorithm
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To achieve motion vector detection, we can utilize the block matching algorithm. This method works by dividing the image into blocks and searching for the best matches within these blocks to determine motion vectors. For successful implementation, we need to define two key parameters: block size and search area size. The block size parameter determines how the image is partitioned into smaller sections, while the search area size affects the range of potential matches we can identify. In practical code implementation, the block matching typically involves calculating similarity metrics (such as Sum of Absolute Differences - SAD or Mean Squared Error - MSE) between the current block and candidate blocks in the reference frame. When selecting optimal block sizes and search area dimensions, developers should conduct thorough experimentation and analysis to determine the most effective parameter values that balance computational efficiency and detection accuracy.
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