Histogram-Based Object Tracking with Adaptive Luminance Scaling
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Solution Overview
Problem
Current technologies fail to effectively track a moving object or arbitrarily sized region-of-interest (ROI) through a sequence of frames due to lack of invariance to object and camera movements, and real-time processing requirements in image analysis tasks such as auto-focus and auto-exposure operations.
Innovation Solution
A method that initializes an ROI with a histogram and luminance measure, identifies putative ROIs in subsequent frames based on these metrics, and selects the best match to maintain tracking, using quality metric values and adaptive luminance scaling to ensure continuous and accurate ROI identification across frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object tracking methods are used to maintain invariance to object and camera movements, then tracking accuracy may be improved, but real-time processing speed deteriorates
Solution Approach 1:
The patent segments the object representation into two independent components: a color histogram (capturing color distribution) and a luminance measure (capturing brightness characteristics). This segmentation allows each component to be processed independently and efficiently, with the histogram providing robustness to movement and the luminance measure enabling rapid comparison, thus achieving both accuracy and real-time performance
Solution Approach 2:
The patent transforms the object representation from spatial domain to statistical domain by computing histogram parameters (color distribution frequencies) and luminance parameters (average brightness, contrast). These parameter transformations create movement-invariant features that can be rapidly compared across frames using simple distance metrics, enabling real-time processing while maintaining tracking accuracy
2Reliability
If complex invariance methods are applied to handle object movement and camera movement, then tracking reliability is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential invariant properties of objects: color distribution (via histogram) and luminance characteristics. By taking out only these critical features and discarding detailed spatial information that varies with movement, the system achieves reliable tracking with minimal computational complexity, avoiding the need for complex transformation models
Solution Approach 2:
The patent pre-computes the color histogram and luminance measure for the target object in the initial frame. These pre-computed parameters serve as reference templates that remain constant throughout tracking, eliminating the need for complex real-time invariance calculations and reducing computational complexity while maintaining reliability
Data Source
AI summary
Systems, methods, and computer readable media to rapidly identify and track an arbitrary sized object through a temporal sequence of frames is described. The object being tracked may initially be identified via a specified or otherwise known region-of-interest (ROI). A portion of that ROI can be used to generate an initial or reference histogram and luminosity measure, metrics that may be used to identify the ROI in a subsequent frame. For a frame subsequent to the initial or reference frame, a series of putative ROIs (each having its own location and size) may be identified and the “best” of the identified ROIs selected. As used here, the term “best” simply means that the more similar two frames' histograms and luminosity measures are, the better one is with respect to the other.