Motion Estimation Using Acceleration Vectors for Mouse Tracking
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Solution Overview
Problem
Existing motion estimation methods for image sequences, particularly in optical mouse systems, face challenges in generating precise candidate motion vectors, leading to divergence during sudden mouse movements, which affects the accuracy and efficiency of motion tracking.
Innovation Solution
The method involves selecting motion vectors from preceding images to generate candidate vectors, including acceleration vectors calculated from the difference between motion vectors associated with previous images, and averaging or adding noise to these vectors to improve correlation and convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If block-by-block motion estimation is used to track mouse movement, then motion tracking capability is achieved, but calculation complexity increases and processing becomes expensive
Solution Approach 1:
The patent extracts only the essential motion information (single motion vector) from the image sequence rather than performing full block-by-block motion estimation. By taking out only the necessary motion parameters needed for mouse tracking, the system achieves motion tracking capability while avoiding the computational complexity of processing every block in the image.
Solution Approach 2:
The patent uses preliminary motion vectors from previous image pairs to generate candidate motion vectors for the current image pair before actual correlation calculation. This preliminary action narrows down the search space and provides initial estimates that guide the motion estimation process, reducing the overall computational burden while maintaining accuracy.
2Productivity
If conventional motion estimation generates candidate vectors from previous motion vectors, then processing efficiency is improved, but accuracy diverges during sudden mouse movements
Solution Approach 1:
The patent dynamically adjusts the candidate motion vector generation strategy based on motion characteristics. By calculating acceleration vectors from changes in previous motion vectors and incorporating them into candidate vectors, the system adapts to sudden movements. This dynamic approach maintains processing efficiency through candidate vector generation while improving accuracy during rapid mouse movements by accounting for acceleration.
Solution Approach 2:
The patent changes the parameters used for candidate vector generation by incorporating acceleration information (second-order motion parameters) in addition to standard motion vectors. This parameter expansion allows the system to handle sudden movements more accurately while maintaining the efficiency benefits of candidate vector-based processing.
3Measurement precision
If acceleration vectors are added to candidate vectors, then tracking accuracy during rapid movements is improved, but calculation complexity increases
Solution Approach 1:
The patent applies partial action by calculating acceleration vectors only for generating candidate motion vectors rather than performing full block-by-block motion estimation with acceleration. This selective application of acceleration-based methods provides improved tracking precision during rapid movements while limiting calculation complexity to only the necessary candidate vector generation stage.
Data Source
AI summary
A motion estimation method is provided for processing successive images in an image sequence, with a motion vector being associated with each of the processed images. For a current image, motion vectors associated with images that precede the current image in the sequence are selected. Candidate motion vectors are generated from the motion vectors that are selected. A motion vector is elected from among the candidate motion vectors. Information that associates the elected motion vector with the current image is stored in memory. At least one of candidate motion vectors is an acceleration vector generated from the acceleration between first and second motion vectors averaged relative to a first and second images, with the first and second images being distinct and preceding the current image in the image sequence. A motion vector averaged relative to a given image is obtained from selected motion vectors associated with images preceding the given image.