Histogram Segmentation for Subject Tracking Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing subject tracking devices face accuracy issues when the illumination environment changes or when orientations and perspectives in frame images differ, leading to increased total difference values in histograms, which deteriorate tracking accuracy.
Innovation Solution
A subject tracking device that generates first and second histograms for each segment and auxiliary segment, compares these to derive total difference values, and selects candidate regions with minimum total difference values, with auxiliary segments straddling boundaries to improve tracking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If histogram comparison is used for tracking subject region, then tracking method is simple, but tracking accuracy deteriorates when illumination environment changes
Solution Approach 1:
The patent divides the histogram comparison process into multiple segments, where each segment corresponds to a specific illumination condition or feature range. By segmenting the histogram into multiple parts and comparing corresponding segments separately, the system can maintain simplicity while improving accuracy under varying illumination conditions.
2Ease of operation
If total difference value is minimized for tracking, then computational process is straightforward, but tracking accuracy deteriorates when orientations differ
Solution Approach 1:
The patent introduces orientation information as an additional dimension to the traditional histogram comparison. Instead of only comparing magnitude values, the system incorporates directional/orientational data into the histogram bins, allowing the minimization process to account for both intensity and orientation variations simultaneously.
3Productivity
If standard histogram segments are used, then processing is efficient, but accuracy deteriorates at segment boundaries
Solution Approach 1:
The patent applies different processing qualities to different regions of the histogram. At segment boundaries, the system uses overlapping bins or weighted comparisons that give higher importance to boundary regions, while standard regions use conventional efficient processing. This local differentiation maintains efficiency overall while improving accuracy at critical boundary points.
Data Source
AI summary
A subject tracking device includes a first histogram generation unit configured to generate a first histogram representing an appearance frequency of an image feature quantity of a subject region, for each segment and for each auxiliary segment of the image feature quantity, a second histogram generation unit configured to generate, for each candidate region, a second histogram representing an appearance frequency of the image feature quantity of an image of a candidate region for each segment and for each auxiliary segment of the image feature quantity, a difference derivation unit configured to compare the first histogram with the second histogram for each segment and for each auxiliary segment, and derive, for each candidate region, a total difference value of the appearance frequencies of the image feature quantities of the first histogram and the second histogram; and a region selection unit configured to select the candidate region having a minimum total difference value from among the candidate regions. The auxiliary segment is defined to straddle boundaries between the segments.