Augmented Reality Object Identification Using Audio Visual Cues
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Solution Overview
Problem
In augmented reality environments, identifying objects of interest can be challenging due to distractions such as crowds, noise, or poor lighting, making it difficult for users to locate and discern specific individuals or objects in real-time.
Innovation Solution
A method and system utilizing cognitive-based computer vision to detect audio and visual cues, such as speech, facial expressions, and gestures, and employing machine learning models to identify and emphasize objects of interest within augmented reality, integrating sensors and networked devices for real-time object recognition and emphasis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If augmented reality is used to enhance real-world objects, then user engagement and information access are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex task of object identification into multiple independent modules: audio cue detection, visual cue detection, and machine learning-based object recognition. Each module processes specific types of information separately before integrating results, reducing the computational burden on any single component while maintaining comprehensive analysis capability.
Solution Approach 2:
The augmented reality system is designed to handle multiple types of objects and environments universally. The same framework can identify speakers in conferences, objects in museums, or landmarks in tourism applications without requiring separate specialized systems, thereby improving adaptability while managing complexity through a unified approach.
2Ease of operation
If real-time object identification is performed in crowded environments, then user experience is improved, but detection accuracy decreases due to distractions
Solution Approach 1:
The system merges multiple detection approaches: audio-based speaker identification, visual-based object recognition, and contextual analysis. By combining these complementary techniques, the system achieves higher detection accuracy in crowded environments than any single method could provide alone, as each method compensates for the weaknesses of others.
Solution Approach 2:
The system incorporates feedback mechanisms where detected objects and speakers are continuously validated against contextual information and user interactions. If detection confidence is low, the system requests additional input or adjusts its detection parameters, ensuring high accuracy while maintaining real-time operation and improving user experience.
Data Source
AI summary
The exemplary embodiments disclose a method, a computer program product, and a computer system for identifying one or more objects of interest in augmented reality. The exemplary embodiments may include detecting one or more cues selected from a group comprising one or more audio cues and one or more visual cues, identifying one or more objects of interest based on the detected one or more cues and a model, and emphasizing the one or more objects of interest within an augmented reality.