Vehicle Location via Image Embedding Distance Models
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Solution Overview
Problem
Existing parking management systems require expensive and time-consuming hardware installations and maintenance, especially for larger parking lots, to detect vehicle presence and availability, which is inefficient.
Innovation Solution
A space analytics system using on-car camera snapshots for feature matching against a pre-calibrated map, estimating vehicle location through real-time image embeddings and dynamic map generation without the need for detailed 3D mapping, utilizing a neural network model for comparison and location determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor-based parking detection systems are installed in each parking space, then vehicle presence and availability can be detected accurately, but hardware installation cost and maintenance time increase significantly
Solution Approach 1:
The patent uses visual copies (images) of parking spaces taken from mobile devices instead of physical sensors in each space. The mobile device captures images that serve as representations of the parking space state, eliminating the need for expensive sensor infrastructure while maintaining detection capability through image processing and recognition algorithms.
Solution Approach 2:
The patent replaces the mechanical sensor-based detection system with an optical/electronic system using mobile device cameras and image processing. Instead of physical sensors detecting vehicle presence, the system uses visual images captured by smartphones or similar devices, processed through computer vision algorithms to determine parking availability.
2Reliability
If multiple cameras and sensors are deployed throughout the parking lot to detect movement, then comprehensive monitoring is achieved, but system cost and maintenance requirements increase
Solution Approach 1:
The patent enables parking space self-monitoring through mobile device imaging. Instead of requiring a complex centralized monitoring system with multiple cameras and sensors, individual users can capture images of parking spaces using their mobile devices, and the system automatically processes these images to determine availability, reducing the need for professional maintenance and system repairs.
Solution Approach 2:
The patent makes mobile devices universal for parking monitoring purposes. Any smartphone or mobile device with a camera can be used to capture parking space images, replacing the need for dedicated infrastructure. The same mobile device can serve multiple users and purposes, eliminating the need for specialized monitoring hardware throughout the parking lot.
3Measurement precision
If detailed 3D mapping of the parking lot is implemented, then accurate location determination is achieved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the essential visual features from parking space images rather than creating comprehensive 3D models. Instead of capturing complete spatial geometry and all dimensional data, the system extracts key visual identifiers and spatial relationships needed for location determination, significantly reducing processing complexity while maintaining sufficient accuracy for parking applications.
Solution Approach 2:
The patent uses partial imaging coverage focused on specific parking space areas rather than complete 3D mapping of the entire parking lot. By capturing images at strategic locations and using selective feature extraction, the system achieves adequate location determination without the excessive computational requirements of full 3D reconstruction and mapping.
Data Source
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AI summary
The present disclosure provides a space analytics system configured to determine the location of a vehicle using on-car camera snapshots to feature match against a pre-calibrated map. The system may estimate the location of a vehicle using the pre-calibrated map based on embeddings from reference images taken of the parking spot. The system may determine the location of the vehicle based on comparing reference image embeddings to real-time image embeddings and determining which comparison yields the embedding distance scores below the required threshold for a match.