User-Described Video Streams with Interactive AI Object Detection
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Solution Overview
Problem
Existing video recognition systems fail to effectively identify and communicate important objects within a video stream, leading to missed opportunities for user interaction and information retrieval due to distractions, placement, and temporal challenges.
Innovation Solution
An adaptive video recognition system using artificial intelligence to identify objects within a video stream, integrate enhanced user interfaces, and apply augmented reality for interactive experiences, leveraging machine learning and neural networks to enhance object identification and communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional video playback is used, then users can watch videos freely, but important objects are missed due to distractions and limited attention
Solution Approach 1:
The system performs self-service by automatically detecting and highlighting important objects in the video stream without requiring user intervention. The AI-based object detection system continuously monitors the video, identifies significant objects based on predefined criteria, and presents them to the user through enhanced interface elements, thereby compensating for limited human attention span.
Solution Approach 2:
The system implements feedback mechanisms by providing real-time information about detected objects through visual highlights, pop-up details, and interactive interfaces. This feedback loop allows users to receive automated notifications about important objects, enabling them to engage with the content without bearing the full cognitive load of continuous attention.
2Measurement precision
If AI-based object detection is added to video streams, then important objects can be identified reliably, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the complex video processing task into distinct functional modules: video streaming component, AI-based object detection component, object attribute extraction component, and enhanced interface presentation component. Each module handles specific aspects of object identification and communication, making the overall complex system more manageable and maintainable.
Solution Approach 2:
The patent introduces an intermediary processing layer between the raw video stream and the user interface. This intermediary component handles the complex AI-based detection and attribute extraction tasks, translating raw video data into structured object information that can be effectively presented to users through the enhanced interface without exposing the underlying complexity.
3Productivity
If real-time object information is provided, then user interaction opportunities increase, but information delivery timing becomes difficult to coordinate
Solution Approach 1:
The system performs preliminary actions by pre-processing the video stream to detect and prepare object information before the user needs it. The AI detection system continuously analyzes the video, and when important objects are identified, the system proactively prepares and presents relevant information through highlights and details panels, eliminating the need for users to manually search or wait for appropriate timing moments.
Data Source
AI summary
A user-described virtual environment method, system, and apparatus obtains a representation of an object and receives a natural language-based communication from a user requesting that a computer-implemented system embody the object within a virtual environment that is described by the user. The natural language description of the virtual environment is interpreted by applying a computer-implemented trained neural network A video stream that embodies the object within a computer-generated virtual environment that is in accordance with the user-described virtual environment is generated by applying a trained neural network and then delivered to the user. The user may then describe desired modifications to the virtual environment and a second video stream is generated in accordance with the desired modifications.