Cooperative Photography System Using Intermediary Server Coordination
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Solution Overview
Problem
Current machine learning solutions for object recognition, such as convolutional neural networks and support vector machines, face high computational complexity and challenging training processes, which hinder their efficiency in classification tasks.
Innovation Solution
A cooperative photography system that uses a network of image capture devices to automatically acquire images based on triggers from other devices, ranking and distributing the best images over a wireless network, leveraging deep learning architectures and neural networks to optimize image capture and ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If crowd-sourced photography is implemented with multiple image capture devices, then image quality and coverage are improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent introduces a server as an intermediary that coordinates between multiple image capture devices. The server receives images from various devices, processes them using machine learning models, and distributes selected images to user devices. This intermediary architecture simplifies the complexity by centralizing coordination tasks rather than requiring direct peer-to-peer communication between all devices.
Solution Approach 2:
The system segments the crowd-sourced photography function into distinct components: image capture devices that collect images, a server that processes and coordinates, and user devices that receive distributed images. This segmentation allows each component to specialize in specific tasks, reducing overall system complexity while maintaining high image quality through coordinated operation.
2Measurement precision
If machine learning models are used for image ranking and selection, then image selection accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by using machine learning models selectively rather than processing all images with the most complex algorithms. The system uses machine learning for ranking and selection where it provides the most value, while simpler filtering and coordination mechanisms handle routine tasks. This approach achieves high selection accuracy without requiring excessive computational resources for every operation.
3Productivity
If automatic image capture triggering is implemented, then productivity and response time are improved, but coordination overhead and network traffic increase
Solution Approach 1:
The system implements preliminary action by having image capture devices automatically trigger image capture based on detected events or conditions without waiting for explicit user commands. Devices can autonomously determine when to capture images based on pre-configured criteria, improving productivity by reducing response time. The server then coordinates the distribution of these pre-captured images, optimizing network traffic by batching transmissions and selecting only necessary images for distribution.
Data Source
AI summary
An intelligent camera network cooperatively acquires images over a wireless network. The network automatically captures images based on a trigger. The trigger may include messages from other image capturing devices. A first image capture devices is triggered to acquire an image based on a message from at least one other image capture device.