Automatic Range and Geo-Referencing for Image Processing
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Solution Overview
Problem
Conventional image processing systems face challenges in efficiently and reliably detecting and classifying objects, particularly in determining the range and geographic location of objects in a scene, especially when dealing with multiple objects simultaneously.
Innovation Solution
The system employs automatic range and geo-referencing methods that combine parameters from different data sources, including camera orientation and location, object classification, and stored laser range finder reference points, to estimate the location and range of objects in a captured image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual operation of laser range finder is used, then range measurement accuracy is improved, but productivity and ease of operation deteriorate due to time-consuming manual processes
Solution Approach 1:
The system enables automatic self-service by having the image capture device itself perform range measurement through integrated laser range finder, eliminating the need for separate manual operations. The processor automatically determines range for each detected object by processing image data and comparing with stored reference data, making the system self-sufficient and automated.
Solution Approach 2:
The system performs preliminary actions by pre-storing reference data from laser range finder measurements of known objects or features in the environment. This reference data is prepared in advance and used during automatic processing to determine the range of detected objects, enabling rapid automated measurement without manual intervention during data collection.
2Productivity
If multiple objects are processed simultaneously, then productivity is improved, but measurement precision and reliability deteriorate due to complexity of handling multiple objects
Solution Approach 1:
The system applies segmentation by processing each detected object independently through separate determination steps. The processor identifies individual objects, determines their respective ranges by comparing with reference data, and calculates locations independently for each object. This segmented approach allows simultaneous processing of multiple objects while maintaining precision through individualized analysis.
3Ease of operation
If automatic processing is implemented, then ease of operation and productivity are improved, but device complexity increases due to multiple data sources and processing steps
Solution Approach 1:
The system merges multiple functions into a single integrated device by combining the image capture device, laser range finder, and processing unit into one unified system. The processor integrates multiple data sources (image data, reference data, object classification) and performs multiple operations (detection, range determination, location calculation) within a single automated workflow, reducing operational complexity despite increased functional integration.
Data Source
AI summary
Systems and methods for automatically determining the physical locations of objects detected within captured images includes a storage system holding geographic reference points associated with the image capture device's field of view, and a logic device generating estimated object locations based on image capture parameters and object information from the image including pixel offsets, object classifications, and geo-reference points, and refining these estimations. The image capture device captures images of scene and generates parameters for its location, height, azimuth, and tilt. A laser range finder or other method may be used to measure distances between the image capture device and geo-reference points in the field of view to populate the storage system. Implementations include video surveillance, traffic monitoring, and other systems.