Computer Vision Pipeline for Autonomous Motion Resource Control
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Solution Overview
Problem
Autonomously motile devices face challenges in efficiently processing image data to determine object properties and perform actions based on these properties, particularly in optimizing the use of computer vision components for specific commands, leading to unnecessary resource consumption and inefficiency.
Innovation Solution
The device determines and configures specific computer vision components to process image data only in response to particular commands, using a pipeline approach where each component processes data based on the output of previous components, optimizing resource usage by only activating relevant components for the required tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all computer vision components are activated to process image data, then comprehensive object detection and tracking are achieved, but resource consumption increases and processing efficiency decreases
Solution Approach 1:
The system dynamically configures computer vision components based on command types and environmental conditions. Different command types (e.g., object detection, tracking, classification) activate different subsets of vision components, allowing the system to adapt its processing power to the specific task at hand, thereby reducing unnecessary resource consumption while maintaining detection accuracy.
Solution Approach 2:
The computer vision system is divided into multiple independent components that can be selectively activated. Each component handles specific aspects of image processing (e.g., feature extraction, object detection, tracking), and only the relevant components are engaged based on the current command requirements, preventing full-system activation for every task.
2Measurement precision
If multiple computer vision components are configured to process image data, then comprehensive object analysis is achieved, but processing time increases
Solution Approach 1:
The system applies partial action by activating only the minimum necessary computer vision components required to fulfill the specific command. For example, simple object detection commands may only require basic feature extraction, while more complex tasks like detailed classification or tracking may engage additional components. This selective approach reduces processing time while maintaining sufficient accuracy for the given task.
3Speed
If computer vision components are continuously activated, then real-time object tracking is maintained, but energy consumption increases
Solution Approach 1:
Instead of continuous activation, the system employs periodic or event-driven activation of computer vision components. Components are activated based on specific triggers such as command reception, detected object significance, or time intervals, allowing the system to maintain readiness for real-time responses while avoiding constant energy-consuming operation of all vision components.
Data Source
AI summary
A device capable of autonomous motion may process image data determined by one or more cameras to determine one or more properties of objects represented in the image data. The device may determine that two or more computer vision components correspond to a particular property. A first computer vision component may process the image data to determine first output data, and the second computer vision component may process the first output data to determine second output data corresponding to the property.