Object-Customized Image Feature Detection for Real-Time Posture Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image feature detection techniques for object detection and posture estimation are often manually optimized and rely on subjective user judgment, leading to reduced reliability and increased processing time, with a lack of real-time performance due to separate algorithm designs for different objects.
Innovation Solution
A system and method that learns an object-customized image feature detection algorithm, utilizing multiple algorithms to detect and estimate the posture of objects in real-time by evaluating and selecting the most suitable algorithm based on computational speed and accuracy, ensuring efficient processing and adaptation to various objects within an input image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual optimization of image feature detection techniques is used, then customization for specific objects is possible, but reliability is reduced and processing time increases
Solution Approach 1:
The system automatically learns and optimizes image feature detection algorithms for specific objects without requiring manual user intervention. The learning module autonomously analyzes object characteristics and selects appropriate detection algorithms, eliminating subjective judgment and improving both reliability and processing efficiency.
2Measurement precision
If separate algorithm designs are created for different objects, then object-specific detection accuracy is improved, but real-time performance is degraded
Solution Approach 1:
The system creates a universal learning module that can adapt to different objects using a standardized framework. Instead of designing separate algorithms for each object, the system uses a single multi-functional learning module that automatically configures itself based on object characteristics, maintaining real-time performance while achieving object-specific accuracy.
Solution Approach 2:
The system performs preliminary learning and algorithm selection in advance before actual detection is needed. By pre-learning object characteristics and pre-selecting appropriate algorithms during the learning phase, the system eliminates time-consuming runtime optimization, ensuring real-time detection performance is maintained.
3Reliability
If manual determination of image features is performed, then expert knowledge can be applied, but processing time is significantly increased
Solution Approach 1:
The system replaces the mechanical process of manual feature determination with an automated learning-based system. The learning module uses computational algorithms to analyze object characteristics and select appropriate image features, substituting human expert manual work with automated intelligence that achieves comparable or superior reliability without the time cost of manual intervention.
Data Source
AI summary
A system for object detection and posture estimation based on an object-customized image feature detection algorithm according to an embodiment of the present disclosure comprises at least one or more processors; and at least one or more memories, wherein at least one application, as an application that is stored in the memory and performs object detection and posture estimation based on an object-customized image feature detection algorithm by being executed by the at least one or more processors, learns a goal object based on a plurality of image feature detection algorithms, generates an algorithm list that includes evaluation of the plurality of image feature detection algorithms for detecting the learned goal object, detects a target object corresponding to the goal object among at least one or more candidate objects within an input image based on the generated algorithm list, and performs a posture estimation process for the detected target object based on the generated algorithm list.