Robot Photographer Semantic Control for Event Capture
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Solution Overview
Problem
Existing robot photographers lack the ability to comprehend the nuances of photography scenes within the realm of social conventions, making it difficult to capture images that accurately reflect specific events.
Innovation Solution
An electronic device equipped with a memory and processors that utilize a large language model to obtain photoshoot suggestions, adjust camera poses, and capture images based on similarity scores between video frames and text embeddings, incorporating user queries and key event descriptors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If robot photographers use traditional detection and navigation methods to capture photos, then they can take basic snapshots, but they fail to comprehend social conventions and capture nuanced event moments
Solution Approach 1:
The patent introduces an intermediary system comprising a language model processor and embedding comparison mechanism that mediates between the video stream input and photo capture decision. This intermediary layer analyzes video frames by comparing text embeddings from user queries with image embeddings from frames, enabling the robot to comprehend social conventions and event nuances without directly complicating the hardware structure.
2Reliability
If robot photographers capture all video frames, then they ensure no event moment is missed, but they waste storage space and processing resources on redundant photos
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously compares video frame embeddings with user query embeddings, uses a language model to generate photoshoot suggestions, and selectively captures photos only when the comparison indicates an event moment is detected. This feedback loop ensures reliable event capture while avoiding redundant photo storage by filtering frames based on semantic relevance.
3Productivity
If robot photographers use simple detection algorithms, then they process video quickly, but they cannot understand user intent and capture relevant event photos
Solution Approach 1:
The patent replaces traditional mechanical computer vision detection algorithms with an intelligence-based system using language models and embedding comparisons. Instead of relying on complex image recognition mechanics, the system substitutes a semantic understanding approach where user queries are converted to text embeddings and compared with video frame embeddings, enabling intent understanding while maintaining processing efficiency.
Data Source
AI summary
An electronic device for controlling a photographic system may obtain a video stream and a user query for a target event, obtain a set of photos from the video stream, obtain at least one photoshoot suggestion based on the user query via a language model, obtain a snapped photo for the target event based on the at least one photoshoot suggestion, in response to a given video frame included in the video stream satisfying a target content criterion, and output one or more photos selected from the set of photos and the snapped photo as event photos.