Generative Interactive Video Recognition With Adaptive Object Highlighting
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Solution Overview
Problem
Existing video recognition systems struggle to effectively identify and communicate important objects within a video stream, as viewers often miss crucial information due to distractions or the fast-paced nature of video content, making it difficult to emphasize and gather further details on objects of interest.
Innovation Solution
An adaptive video recognition system using artificial intelligence to identify objects within a video stream, apply machine learning techniques, and communicate object attribute information to users through a fuzzy content network, allowing for interactive and personalized experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video recognition systems are used to identify objects in video streams, then basic object detection is achieved, but important objects are missed due to viewer distractions and fast-paced content
Solution Approach 1:
The system implements feedback loops where user interactions (clicks, hovers, selections) on identified objects provide continuous feedback to refine and update object importance weights. This allows the system to learn from user behavior patterns and improve object identification accuracy over time, ensuring important objects are not missed
Solution Approach 2:
The system dynamically changes parameters by adjusting object importance weights based on multiple factors including user interaction data, contextual analysis, and temporal patterns. This parameter adjustment mechanism allows the system to adaptively prioritize important objects in fast-paced video content, maintaining high identification accuracy despite rapid scene changes
2Loss of information
If the system emphasizes certain important objects in the video stream, then information about key objects is highlighted, but the system complexity increases due to multiple processing layers
Solution Approach 1:
The system segments the complex video analysis task into distinct functional modules: object detection module, attribute recognition module, importance weighting module, and information delivery module. Each module handles a specific aspect of processing, making the overall system more manageable and maintainable while preserving comprehensive object information
Solution Approach 2:
The system introduces an intermediary object importance weighting mechanism that mediates between raw video data and final information delivery. This intermediary layer processes and prioritizes object attributes before delivery, reducing the complexity burden on individual components while ensuring complete information retention
3Speed
If real-time object recognition is implemented in fast-paced video content, then timely information delivery is achieved, but information loss occurs due to the rapid transition of video moments
Solution Approach 1:
The system performs preliminary object detection and attribute extraction as video frames are being decoded, before full rendering occurs. This preliminary action captures essential object information in advance, allowing timely delivery without losing detail information even in fast-paced sequences
Solution Approach 2:
The system maintains continuous object tracking and information accumulation across rapidly transitioning frames. By continuously updating object states and accumulating attribute information over time, the system ensures no detail information is lost despite the rapid pace of video content
4Loss of information
If comprehensive object information is gathered from the video stream, then complete data is available, but user attention is overwhelmed by the volume of information
Solution Approach 1:
The system applies local quality by delivering different levels of object information detail to different users based on their specific interests, interaction history, and contextual needs. Instead of uniform information delivery, each user receives tailored information appropriate to their local context, maintaining completeness while avoiding overload
Data Source
AI summary
A generative interactive video computer-implemented method and system applies trained computer-implemented neural networks to generate a sequence of images, which may comprise a video, and which contains identified objects, and then delivers the sequence of images to a user. The sequence of images may be further generated in accordance with an inference of a preference from user behavioral information. The identified objects may be provided to the system in the form of natural language and/or images. The system then generates and delivers to users natural language-based responses to user requests for information with respect to the identified objects, including attributes that are associated with the identified objects. Users may direct the system to generate video-based virtual environments that include representations of the identified objects.