Image Tracking Device Multi-Dimensional Variance Storage
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Solution Overview
Problem
Conventional image tracking technologies fail to memorize and learn feature-related variances of objects in videos, leading to inefficient tracking processes and resource wastage when objects exhibit repeated feature variances.
Innovation Solution
An image tracking device and method that utilize a multi-dimensional storage space to store and calculate multi-dimensional variances of feature-related changes such as translation, zooming, blur, rotation, panning, tilting, and illumination, allowing for the identification and storage of objects based on these variances, thereby reducing tracking time and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional image tracking technologies calculate feature-related variance solely for determining current motion, then the tracking process can be kept simple, but the system fails to memorize and learn feature variances, leading to repeated calculations and resource wastage
Solution Approach 1:
The system performs preliminary actions by calculating feature-related variances in advance and storing them in a multi-dimensional storage space before they are needed for tracking decisions. This allows the system to memorize feature variances and avoid recalculating them repeatedly, thereby reducing tracking time without requiring complex real-time processing
Solution Approach 2:
The patent introduces a multi-dimensional storage space that organizes feature-related variances along multiple dimensions (translation, zooming, rotation, illumination, etc.). This dimensional organization enables efficient storage and retrieval of feature variances, allowing the system to handle complex tracking scenarios while maintaining manageable system complexity through structured data organization
2Productivity
If conventional image tracking technologies do not store feature-related variances, then the system remains simple and resource-efficient for single-instance tracking, but it cannot recognize repeated feature variances, causing redundant calculations and resource wastage
Solution Approach 1:
The system creates copies of feature-related variances and stores them in a multi-dimensional storage space. When tracking an object, the system can compare current feature variances against stored copies to determine if the same variance has been encountered before, avoiding redundant calculations and reducing resource consumption while improving tracking efficiency
Solution Approach 2:
The system implements feedback by storing calculated feature-related variances and using them to inform future tracking decisions. When a feature variance is encountered again, the system retrieves the stored information to confirm the object's identity or motion pattern, creating a feedback loop that improves tracking efficiency and reduces resource consumption by avoiding repeated full calculations
Data Source
AI summary
An image tracking device and an image tracking method thereof are provided. The image tracking device includes an image capture interface, a storage means, and a processor means. The storage means has a multi-dimensional storage space for storing a plurality of first images, each dimension of the multi-dimensional storage space being corresponding to a feature-related variance of a multi-dimensional variance. The processor means is configured to execute the following operations: marking a second image in the picture frame; calculating a multi-dimensional variance between the second image and each of the first images separately; determining whether the second image contains the object according to the multi-dimensional variance calculated; and if the second image is determined as one containing the object, storing the second image as one of the first images, in a specific subspace of the multi-dimensional storage space according to the multi-dimensional variance calculated.