Mean Shift Object Tracking with Foreground Map Constraints
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Solution Overview
Problem
Existing mean shift-based visual tracking methods face challenges in handling fast-moving objects and scale determination, often resulting in shrinkage problems and accumulation errors due to their reliance on time-consuming scale space searches and noise-sensitive algorithms.
Innovation Solution
The proposed solution involves a mean shift-based object tracking method with soft constraints from a foreground map, scale adaptation using a discriminant function to enhance target-background discrimination, and conservative updating of the color histogram-based appearance model to reduce drifting artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scale space searches are performed to determine the best scale, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent changes the parameter approach by using a fixed scale or limited scale set instead of exhaustive scale space searches. The mean shift algorithm operates on predetermined scales, transforming the continuous scale optimization problem into a discrete selection among limited scale options, thereby reducing computational time while maintaining acceptable accuracy.
Solution Approach 2:
Instead of performing complete scale space searches across all possible scales, the patent applies partial action by searching only at selected scales (original scale and limited variations). This partial search approach achieves sufficient tracking accuracy without the full computational burden of exhaustive scale space exploration.
2Adaptability or versatility
If multiple kernels are used to model target appearance and motion characteristics, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent extracts and separates the scale determination function from the motion modeling function. By using a fixed or limited set of scales independent of the motion model, it removes the complexity of coupling multiple kernels with scale optimization, simplifying the overall system while maintaining adaptability through the mean shift algorithm's ability to track motion at the selected scales.
3Adaptability or versatility
If histogram updating is performed frequently to track target appearance changes, then adaptability is improved, but manufacturing precision deteriorates due to accumulation errors
Solution Approach 1:
The patent implements feedback control in histogram updating by comparing the current histogram with the initial target histogram and only updating when significant changes are detected. This feedback mechanism prevents accumulation of errors by selectively updating the appearance model based on actual target changes rather than continuous updates, thereby maintaining tracking accuracy while adapting to appearance variations.
Data Source
AI summary
An image processing method can be performed on a video image that includes an initial frame and a plurality of subsequent frames. An object is located within the initial frame of the video image and a histogram related to the object is generated. A foreground map that includes the object is also generated. For each subsequent frame, a mean shift iteration is performed to adjust the location of the object within the current frame. The histogram related to the object and the foreground map can then be updated.