Google llc (20240346832). Detection Network Based On Embedding Distance Models simplified abstract
Contents
Detection Network Based On Embedding Distance Models
Organization Name
Inventor(s)
Dongeek Shin of San Jose CA (US)
Detection Network Based On Embedding Distance Models - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240346832 titled 'Detection Network Based On Embedding Distance Models
The present disclosure introduces a space analytics system that utilizes on-car camera snapshots to determine the location of a vehicle by matching them against a pre-calibrated map.
- The system estimates the vehicle's location based on embeddings from reference images of the parking spot.
- It compares reference image embeddings to real-time image embeddings to determine the vehicle's location.
- The system identifies a match when the embedding distance scores fall below a required threshold.
Potential Applications: - Automated parking systems - Vehicle tracking and navigation - Traffic management and optimization
Problems Solved: - Efficient vehicle location determination - Enhanced parking spot identification - Improved navigation accuracy
Benefits: - Increased parking efficiency - Reduced search time for parking spots - Enhanced overall traffic flow
Commercial Applications: Title: Advanced Vehicle Location System for Smart Parking Solutions This technology can be utilized in smart parking solutions for commercial parking lots, shopping malls, airports, and other high-traffic areas to streamline parking processes and improve customer experience.
Questions about the technology: 1. How does the system ensure accurate matching of reference and real-time image embeddings? The system uses advanced algorithms to analyze and compare the embeddings, ensuring accurate location determination. 2. What are the potential challenges in implementing this technology in real-world parking scenarios? Implementing this technology may require integration with existing parking systems and infrastructure, as well as addressing privacy concerns related to vehicle tracking.
Original Abstract Submitted
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.
