Line Tracking via Graph Matching Cycle Detection
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Solution Overview
Problem
Conventional line tracking systems lack robustness for augmented reality applications, especially when objects have little texture, as they track lines independently rather than in groups, leading to reduced accuracy and reliability.
Innovation Solution
A vision-based tracking system that detects and groups lines by computing intersection points, forming connected line groups, and using graph matching and cycle detection for robust tracking, enabling the system to track objects more accurately by comparing line groups across frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional line tracking is used to track objects with little texture, then the system can operate on textureless objects, but the tracking robustness and accuracy deteriorate
Solution Approach 1:
The patent merges multiple individual line tracks into grouped line structures by identifying and clustering lines that share common intersection points. This combining approach transforms weak individual line signals into stronger grouped features, improving tracking robustness while maintaining the ability to track textureless objects.
Solution Approach 2:
The patent transitions from tracking one-dimensional lines to tracking two-dimensional line groups formed by intersection points. By adding the dimension of intersection point connectivity, the system creates more stable and reliable tracking features from otherwise textureless object surfaces.
2Device complexity
If individual lines are tracked independently, then the tracking algorithm remains simple, but the tracking accuracy and reliability deteriorate
Solution Approach 1:
The patent segments the line tracking problem into distinct processing stages: line detection, intersection point computation, line grouping based on shared intersections, and tracking of grouped lines. This segmentation maintains algorithmic clarity while systematically improving tracking accuracy through progressive feature refinement.
Solution Approach 2:
The patent performs preliminary grouping of lines by intersection points before the actual tracking process. By pre-organizing lines into stable groups based on their geometric relationships, the system establishes accurate tracking features in advance, improving measurement precision without significantly increasing overall algorithmic complexity.
3Reliability
If groups of lines are formed using intersection points, then the tracking robustness improves, but the computational complexity increases
Solution Approach 1:
The patent employs self-service mechanisms where intersection points automatically serve as the basis for grouping lines. The geometric relationships among lines naturally generate intersection points that define groups, eliminating the need for external grouping criteria or manual parameter tuning, thus improving robustness without proportional increases in computational complexity.
Data Source
AI summary
A vision based tracking system in a mobile platform tracks objects using groups of detected lines. The tracking system detects lines in a captured image of the object to be tracked. Groups of lines are formed from the detected lines. The groups of lines may be formed by computing intersection points of the detected lines and using intersection points to identified connected lines, where the groups of lines are formed using connected lines. A graph of the detected lines may be constructed and intersection points identified. Interesting subgraphs are generated using the connections and the group of lines is formed with the interesting subgraphs. Once the groups of lines are formed, the groups of lines are used to track the object, e.g., by comparing the groups of lines in a current image of the object to groups of lines in a previous image of the object.