Remote Camera Distance Estimation Using Label Dimensions
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Solution Overview
Problem
Conventional depth-sensing cameras are limited by their small range and high power consumption, making them unsuitable for mobile or wearable devices, and they fail to accurately estimate object distances in environments with visual constraints or limited visibility.
Innovation Solution
A method and system that uses a remote camera to determine object distances by analyzing label images, calculating distances based on optical characteristics, label dimensions, and image dimensions, and announces these distances through output components, allowing for object orientation and location determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth-sensing cameras are used to estimate object distances, then measurement precision is improved, but use of energy increases and device complexity increases
Solution Approach 1:
The patent uses a standard camera to capture images of objects with known labels, then creates a computational model that copies the depth-sensing capability through software processing rather than hardware sensors. The system captures 2D images and uses label recognition with optical flow analysis to infer 3D distance information, replacing expensive depth-sensing hardware with a software-based solution that runs on mobile devices.
2Measurement precision
If depth-sensing cameras are used to estimate object distances, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent makes a standard camera perform multiple functions: it captures both 2D images for visual recognition and uses those same images for 3D distance estimation through computational methods. The label recognition system serves dual purposes of object identification and depth calculation, eliminating the need for separate depth-sensing hardware and reducing overall system complexity.
Solution Approach 2:
The system copies depth-sensing functionality through software algorithms that process standard camera images. By using optical flow analysis and label dimension comparison on regular 2D images, the system replicates 3D measurement capabilities without requiring specialized depth-sensing cameras, thereby reducing device complexity while maintaining measurement precision.
3Ease of operation
If conventional cameras are used, then ease of operation is maintained, but measurement precision of object distance deteriorates
Solution Approach 1:
The patent transforms the problem by changing parameters: instead of trying to extract distance directly from pixel coordinates in a standard camera image, it introduces label dimension as an additional parameter. By comparing the known real-world dimensions of recognized labels with their apparent size in the image, the system calculates distance using perspective projection geometry, enabling standard cameras to provide accurate distance estimates while maintaining ease of operation.
Data Source
AI summary
In determining a distance of an object captured by a remote camera, a controller receives an image of the object from another controller coupled to a camera over a data network. The image includes a label image of a label associated with the object. The controller determines a label dimension of the label that includes a real world size of the label and determines a label image dimension of the label image that includes a size of the label image. The controller calculates a label distance using optical characteristics of the camera, the label dimension, and the label image dimension, and announces the label distance using an output component coupled to the controller. When the controller receives a command to operate the camera input by a user, the controller sends at least one instruction to operate the camera according to the command to the other controller over the data network.