Image Target Tracking Using Low-Resolution Region Preselection

Solution Overview

Problem

Existing image processing technologies for tracking objects require complex apparatus configurations and high processing loads, making them inefficient for accurate target specification.

Innovation Solution

A processing apparatus that converts high-resolution images into lower-resolution images, allowing for efficient target specification and tracking by reducing processing load, while also enabling gesture recognition and control of mobile objects.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If image processing is performed on high-resolution images to accurately specify targets, then measurement precision is improved, but processing load increases

Engineering Contradiction:

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The image processing is segmented into two stages: first processing low-resolution images to obtain target region candidates, then processing only those specific regions in high-resolution images. This division reduces the overall processing load while maintaining accurate target specification.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Different resolution qualities are applied to different regions of the image. Low-resolution processing is applied to the entire image for initial target region detection, while high-resolution processing is applied only to identified target regions for final accurate specification.

Inventive Principle:

Principle #3Local quality

2Measurement precision

If complex image processing algorithms are used to track objects accurately, then measurement precision is improved, but device complexity increases

Engineering Contradiction:

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The tracking system is segmented into a coarse tracking component that operates on low-resolution images and a fine tracking component that operates on high-resolution images. This segmentation simplifies each individual component while achieving accurate overall tracking.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Low-resolution images serve as an intermediary to guide the processing of high-resolution images. The target regions identified in low-resolution images act as intermediaries that direct where detailed high-resolution analysis should be focused.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If high-resolution images are processed in real-time for gesture recognition, then measurement precision is improved, but loss of time increases

Engineering Contradiction:

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

High-resolution processing is applied locally only to regions containing gestures rather than the entire image. This maintains accurate gesture recognition while reducing the time required for processing.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

Target regions are preliminarily identified using low-resolution images before performing detailed gesture recognition on high-resolution images. This preliminary action reduces the amount of data that requires time-consuming high-resolution processing.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12211318B2Processing apparatus, mobile object, processing method, and storage medium

Publication Date: 2025.01.28

AI summary

A processing apparatus converts a first image into a second image with a lower resolution than a resolution of the first image, specifies a target region including a predetermined objective target in the second image on the basis of the second image, and specifies a target region including the objective target in the first image on the basis of the specified target region in the second image.