Vehicle Shadow Refinement for Accurate Traffic Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current traffic management systems face challenges in accurately determining vehicle characteristics, such as height, weight, and type, due to noise and speckle in lower resolution raster imagery, which affects the reliability of shadow representation and subsequent navigation and route planning.
Innovation Solution
A method and system that processes raster imagery to refine the representation of vehicle shadows by modifying pixel values based on the vehicle's shape, aligning the shadow edge with the vehicle outline, and using a reference vehicle's shadow correlation to determine vehicle characteristics like height, width, and type, enabling more accurate and efficient traffic management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lower resolution raster imagery is used, then processing resources are conserved and computational efficiency is improved, but the accuracy of vehicle characteristic determination deteriorates due to noise and speckle in shadow representation
Solution Approach 1:
The system performs preliminary actions by extracting the vehicle outline before processing the shadow. This pre-extracted geometric information is then used to guide the shadow refinement process, allowing the system to start with a known reference shape that will be used to correct the noisy shadow data.
Solution Approach 2:
The vehicle outline acts as an intermediary between the raw shadow data and the final vehicle characteristics. The outline serves as a mediating geometric representation that bridges the gap between the noisy raster shadow and the accurate vehicle dimensions needed for traffic management.
Solution Approach 3:
The system changes parameters by modifying the shadow representation from a noisy pixel-based image to a refined geometric shape that conforms to the vehicle outline. This parameter transformation converts irregular shadow data into standardized dimensional measurements that can be reliably used for vehicle classification.
2Measurement precision
If high resolution imagery is used, then vehicle characteristics can be determined with higher accuracy, but processing resources are consumed and computational complexity increases
Solution Approach 1:
The system extracts only the essential geometric information from the high-resolution imagery - specifically the vehicle outline and shadow boundary. By taking out only these critical geometric features rather than processing the entire high-resolution image, the system achieves accurate vehicle characteristic determination without the computational burden of processing all image data.
Solution Approach 2:
The processing is segmented into distinct stages: first extracting the vehicle outline, then processing the shadow separately, and finally combining these elements to determine vehicle characteristics. This segmentation allows each component to be processed with appropriate complexity levels, reducing overall computational requirements.
3Speed
If raster imagery processing is used without shadow refinement, then processing speed is improved, but the reliability of shadow representation and subsequent navigation planning deteriorates
Solution Approach 1:
The vehicle outline is extracted as a preliminary step before shadow refinement. This pre-extracted outline provides a reliable geometric reference that guides the subsequent shadow processing, ensuring that the final shadow representation accurately reflects the vehicle's true shape and dimensions.
Solution Approach 2:
The system uses feedback by comparing the extracted vehicle outline with the processed shadow representation. The outline serves as a reference against which the shadow is validated and refined, creating a feedback loop that ensures the shadow accurately represents the vehicle's actual geometry.
Data Source
AI summary
A method, system and computer program product are configured to analyze an image of a vehicle to determine a characteristic of the vehicle, such as may be represented by or otherwise at least partially defined by the shadow cast by the vehicle. In the context of a method, information is received identifying a vehicle from a raster image and the pixel values of the raster image are evaluated to identify pixels having pixel values representative of a shadow associated with the vehicle. The method also modifies a representation of the shadow by modifying the pixel values of the pixels based upon a shape of the vehicle such that the representation of the shadow, as modified, has a shape corresponding to the shape of the vehicle. The method additionally determines a characteristic of the vehicle based upon the representation of the shadow, as modified, that is associated with the vehicle.