Actor Input Feature Identification for Uninterrupted Recognition
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Solution Overview
Problem
Current systems require extensive training and disrupt normal operation to identify targets like objects or persons in images, necessitating a method for efficient identification without interrupting workflow.
Innovation Solution
A method that defines and executes actor inputs, detects salient features, and creates a model using these features, allowing for efficient identification and command execution within defined parameters, utilizing sensors and processors to retain and apply this data for future recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a system is provided with training images in advance for target identification, then the system can be trained to identify targets, but the system operating time increases and normal functions are disrupted
Solution Approach 1:
The patent applies preliminary action by pre-defining actor inputs and their associated salient features before actual target identification is needed. The system pre-processes and stores characteristic features of potential targets during normal operation, so that when identification is required, the pre-prepared feature data can be quickly retrieved and matched without interrupting workflow or requiring dedicated training time.
2Measurement precision
If a system is provided with training images in advance for target identification, then the system can be trained to identify targets, but processing resources and power consumption increase
Solution Approach 1:
The patent extracts and stores only the essential salient features of actors and targets rather than processing and storing complete training images. By identifying and retaining only the characteristic features that are necessary for identification (such as key visual markers or behavioral patterns), the system reduces the amount of data that needs to be processed and stored, thereby lowering power consumption and processing resource requirements while maintaining identification accuracy.
3Measurement precision
If a system is provided with training images in advance for target identification, then the system can be trained to identify targets, but the system complexity increases
Solution Approach 1:
The patent segments the target identification process into distinct components: defining actor inputs, detecting actors, identifying salient features, and matching features to targets. By breaking down the complex training and identification process into these modular segments, the system can handle each aspect independently and efficiently, reducing overall system complexity while maintaining the ability to accurately identify targets through systematic feature matching.
Data Source
AI summary
Disclosed are methods and apparatuses to recognize actors during normal system operation. The method includes defining actor input such as hand gestures, executing and detecting input, and identifying salient features of the actor therein. A model is defined from salient features, and a data set of salient features and/or model are retained, and may be used to identify actors for other inputs. A command such as “unlock” may be executed in response to actor input. Parameters may be applied to further define where, when, how, etc. actor input is executed, such as defining a region for a gesture. The apparatus includes a processor and sensor, the processor defining actor input, identifying salient features, defining a model therefrom, and retaining a data set. A display may also be used to show actor input, a defined region, relevant information, and/or an environment. A stylus or other non-human actor may be used.