Parking Event Imaging Using Geolocation-Triggered Multi-View Capture
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Solution Overview
Problem
Current parking enforcement systems provide limited contextual information about vehicles due to fixed positioning of color cameras, capturing only one view of the vehicle, which is inadequate for comprehensive violation detection.
Innovation Solution
A parking enforcement system equipped with multiple cameras or a 360-degree field of view camera system captures multiple views of a target vehicle, utilizing geolocation and temporal data to initiate additional image collection when positional or temporal thresholds are met, enhancing the context-rich parking event generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a fixed-position color camera is used for capturing license plate information, then the system structure is simple, but the contextual information about the vehicle is limited to only one view
Solution Approach 1:
The camera system is divided into multiple independent cameras (first context camera, second context camera, infrared camera) positioned at different orientations. Each camera captures images from its specific viewpoint, and the system segments the vehicle into multiple visible portions across different images, thereby comprehensively capturing contextual information without requiring a single complex 360-degree camera.
2Loss of information
If multiple cameras are deployed to capture different views of the vehicle, then the contextual information is improved, but the system complexity and cost increase
Solution Approach 1:
The system dynamically determines which images to capture based on the patrol vehicle's movement. As the patrol vehicle moves relative to the target vehicle, the system activates different context cameras at different times to capture images from optimal viewpoints. This dynamic approach allows comprehensive coverage using a limited number of cameras rather than requiring all cameras to be active simultaneously.
Solution Approach 2:
The system pre-positions multiple context cameras at different orientations (first context camera for first orientation, second context camera for second orientation) and pre-captures images when the patrol vehicle is at favorable positions. These preliminary captured images are stored and later associated with the parking violation event, eliminating the need for continuous complex real-time processing.
3Loss of information
If continuous image capture is performed to ensure all vehicle views are captured, then the completeness of contextual information is improved, but the data processing load and storage requirements increase
Solution Approach 1:
The system uses feedback from geolocation data and temporal data to intelligently control image capture. The processor continuously monitors the patrol vehicle's position and movement, and only triggers context camera activation when movement thresholds are met, ensuring that captured images are both necessary and sufficient for the violation event without generating excessive redundant data.
Solution Approach 2:
Instead of continuously capturing images from all cameras, the system captures only the partial set of images necessary to document the violation. By using movement thresholds and geometric relationships between camera positions and target vehicle, the system captures sufficient views to identify vehicle properties and confirm parking violations without the excessive data burden of continuous omnidirectional capture.
Data Source
AI summary
A method of generating a context-rich parking event of a target vehicle taken by a patrol vehicle; it has obtaining a plate read event identifying an identifier of the target vehicle; initiating a collection of a first context image of a first view of the target vehicle; obtaining of geolocation information; obtaining temporal information; verifying if at least one condition is met by calculating if at least one of: a temporal constraint threshold is reached by using the temporal information; and a position constraint threshold is reached by using the geolocation information; initiating a collection by the patrol vehicle of a second context image of a second view of the target vehicle; and causing an association between the second context image and the parking event to generate the context-rich parking event.