Statistical Target Signatures for Image Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing object recognition and tracking systems rely on insufficient data and unreliable analysis methods, such as edges, blobs, and cross-correlation, which fail to generate statistically significant signatures for detecting objects across multiple image frames or visual data, leading to unreliable object identification and tracking.
Innovation Solution
The system generates statistically significant signatures for targets of interest using series approximations, such as Fourier Series and Gram-Charlier Series, of image data, allowing for confident identification and tracking of objects despite translation and rotation, and maintaining a target lock even under occlusion or perturbations, using intensity values from various sensors and cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object recognition techniques (edges, blobs, cross-correlation) are used, then the system is simple to implement, but the reliability of object identification and tracking deteriorates due to insufficient data and unreliable analysis
Solution Approach 1:
The system performs preliminary actions by generating statistically significant signatures from training image data before actual target detection. These pre-computed signatures serve as reference patterns that improve reliability during runtime without adding computational complexity to the real-time detection process.
Solution Approach 2:
The patent transitions from traditional 2D image space analysis to a higher-dimensional statistical signature space. By computing statistical signatures that capture intensity distributions and spatial relationships across multiple dimensions, the system achieves more reliable object identification while maintaining implementation feasibility through efficient statistical computations.
2Measurement precision
If traditional analysis methods are used, then the processing speed is fast, but the measurement precision of object detection deteriorates
Solution Approach 1:
The patent replaces traditional mechanical image processing methods (edge detection, blob analysis) with statistical signal processing techniques. By substituting geometric-based methods with probability-based signature matching, the system achieves higher measurement precision while keeping the implementation complexity manageable through efficient statistical algorithms.
3Reliability
If simple feature extraction is used, then the computational efficiency is high, but the reliability of tracking across multiple frames deteriorates
Solution Approach 1:
The system performs preliminary computation of statistical signatures during a training phase using offline image data. This preliminary action creates robust reference signatures that can be quickly matched against test images, ensuring reliable tracking across multiple frames while maintaining high processing throughput during runtime.
Solution Approach 2:
The patent creates copies of target signatures from training data that can be rapidly compared against new image frames. These signature copies serve as reusable reference patterns that enable fast, reliable tracking without requiring complex real-time analysis of each individual frame.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A facility implementing systems and/or methods for creating statistically significant signatures for targets of interest and using those signatures to identify and locate targets of interest within image data, such as an array of pixels, captured still images, video data, etc., is described. Embodiments of the facility generate statistically significant signatures based at least in part on series approximations (e.g., Fourier Series, Gram-Charlier Series) of image data. The disclosed techniques allow for a high degree of confidence in identifying and tracking targets of interest within visual data and are highly tolerant of translation and rotation in identifying objects using the statistically significant signatures.