3D Point Tracking From Moving Vehicle Image Streams
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Solution Overview
Problem
Determining accurate three-dimensional locations of objects within images from a moving camera-equipped vehicle, such as drones, is challenging due to the vehicle's movement and the need for costly and heavy laser systems, which are impractical for deployment on unmanned vehicles.
Innovation Solution
An image processing system uses machine learning models to identify objects, determine their orientations, and estimate distances by analyzing image streams from different angles, combining metadata to calculate accurate three-dimensional positions, even when objects are not fully visible or have unknown dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heavy and costly laser systems are used to determine three-dimensional location, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces heavy mechanical laser ranging systems with a computational approach using standard camera imaging combined with machine learning object identification and geometric calculations. The system uses image metadata (camera position, orientation, field of view) and object dimensions from database lookups to calculate three-dimensional locations through coordinate transformation mathematics rather than physical laser measurement.
Solution Approach 2:
The patent creates a virtual three-dimensional model of the operational area by projecting two-dimensional image coordinates into three-dimensional space using camera metadata and geometric relationships. This virtual model replicates the functionality of physical laser ranging systems without requiring the heavy hardware infrastructure.
2Measurement precision
If laser systems are equipped on vehicles, then three-dimensional location determination is improved, but weight increases and operational time decreases
Solution Approach 1:
The patent substitutes physical laser ranging hardware with computational methods running on standard vehicle-mounted processors. The system uses the vehicle's existing camera and its metadata (position, orientation, field of view parameters) to perform all distance and location calculations through mathematical coordinate transformations, eliminating the need for heavy laser emitters and detectors.
Solution Approach 2:
The system utilizes the vehicle's own existing sensors and computing resources (camera, processor, stored camera metadata) to perform three-dimensional location determination independently, without requiring external heavy equipment. The vehicle serves its own measurement needs using its inherent capabilities.
3Adaptability or versatility
If the vehicle is moving and susceptible to vibration, then operational flexibility is improved, but measurement stability deteriorates
Solution Approach 1:
The patent is specifically designed for dynamic conditions by using the vehicle's real-time camera metadata (current position, orientation, field of view) captured at the moment each image is taken. The system calculates three-dimensional locations based on the actual dynamic state of the vehicle when the image was acquired, rather than requiring the vehicle to be stationary. This allows accurate measurements even during movement and vibration.
Solution Approach 2:
The system pre-stores camera metadata (intrinsic parameters like focal length and extrinsic parameters like mounting position) and object dimension data in databases before operation. During dynamic operation, it rapidly retrieves and applies this pre-prepared information combined with real-time camera state to immediately calculate three-dimensional locations without requiring stabilization.
Data Source
AI summary
Methods and systems are described herein for determining three-dimensional locations of objects within identified portions of images. An image processing system may receive an image and an identification of location within an image. The image may be input into a machine learning model to detect one or more objects within the identified location. Multiple images may then be used to generate location estimations of those objects. Based on the location estimations, an accurate three-dimensional location may be calculated.