Intelligent Photography Using Machine Learning for Dynamic Capture
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Solution Overview
Problem
Existing automated photography systems are inefficient in capturing photo-worthy moments in dynamic environments, such as theme parks and sporting events, as they rely on fixed cues and are unresponsive to real-time circumstances, potentially disrupting the experience and failing to capture optimal moments.
Innovation Solution
A machine learning model is trained using historical video data to identify photo-worthy moments and parameters, allowing for programmatic capture of pictures based on real-time video streams from control cameras, using techniques like convolutional neural networks to recognize behaviors and events, and instructing cameras to take pictures at optimal times and angles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated photography systems use fixed cues (e.g., regular intervals) for capturing pictures, then the system operates automatically without human intervention, but the system fails to capture photo-worthy moments and is unresponsive to real-time circumstances
Solution Approach 1:
The system transitions from static fixed-interval capture to dynamic event-driven capture by using machine learning models that continuously analyze video streams and adaptively determine optimal capture moments based on detected behaviors, expressions, and scene changes
Solution Approach 2:
The patent replaces traditional mechanical timing-based automation with intelligent software-based automation using trained machine learning models that process visual data and make adaptive decisions about when and how to capture images
2Manufacturing precision
If photographers manually observe subjects to determine photo-worthy events, then photo-quality moments are captured, but the process is time-consuming and cannot keep up with frequent events in dynamic environments
Solution Approach 1:
The system replaces manual human observation with automated machine learning-based visual analysis that processes video streams in real-time, enabling both high-quality detection and rapid response to multiple events simultaneously
Solution Approach 2:
The machine learning models autonomously analyze video data, detect photo-worthy moments, and trigger camera captures without human intervention, enabling the system to self-manage the photography process at high speed
3Reliability
If people are made aware they are being photographed, then photo capture can be confirmed, but the experience is disrupted and user satisfaction decreases
Solution Approach 1:
The system uses hidden control cameras that capture video streams for analysis without being visible to subjects, serving as an intermediary that enables automated detection while keeping subjects unaware and their experience undisturbed
Data Source
AI summary
Embodiments of the present disclosure relate to intelligent photography with machine learning. Embodiments include receiving a video stream from a control camera. Embodiments include providing inputs to a trained machine learning model based on the video stream. Embodiments include determining, based on data output by the trained machine learning model in response to the inputs, at least a first time for capturing a first picture during a session. Embodiments include programmatically instructing a first camera to capture the first picture at the first time during the session.