AI Camera Dynamic Adjustment for Parking Recognition Accuracy
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Solution Overview
Problem
Conventional parking systems struggle with dynamic environmental conditions such as varying lighting, network connectivity issues, and movement of objects, leading to reduced accuracy in license plate recognition and overall system performance.
Innovation Solution
The implementation of dynamic AI-enabled cameras with License Plate Recognition (LPR) capabilities that can detect environmental conditions and automatically adjust camera settings, such as focus, zoom, and network caching, to improve image quality and identification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional static cameras are used in parking systems, then device complexity is reduced, but measurement precision and reliability deteriorate due to inability to adapt to dynamic environmental conditions
Solution Approach 1:
The patent implements dynamic camera systems that automatically adjust focus, zoom, and other settings in real-time based on detected environmental conditions such as lighting changes, object movement, and network status. This transforms static cameras into adaptive systems that maintain high recognition accuracy across varying conditions without requiring manual intervention.
Solution Approach 2:
The system incorporates feedback mechanisms where camera performance is continuously monitored and environmental conditions are detected. Based on this feedback, the camera automatically adjusts its settings to optimize license plate recognition accuracy. The system learns from past performance and adapts to maintain precision despite environmental variations.
2Adaptability or versatility
If camera settings are manually adjusted for each site, then adaptability to environmental conditions improves, but ease of operation and deployment time worsen
Solution Approach 1:
The patent enables cameras to automatically detect environmental conditions and self-adjust their settings without requiring manual configuration. The system autonomously adapts to different lighting conditions, parking lot layouts, and network environments, eliminating the need for technicians to manually configure each camera site-by-site while maintaining high adaptability.
Solution Approach 2:
The system dynamically changes camera parameters such as focus distance, zoom level, and exposure settings based on detected environmental conditions. This automatic parameter adjustment allows the camera to adapt to various environments without manual intervention, simplifying deployment while maintaining versatility.
3Measurement precision
If high-resolution video transmission is maintained during high traffic times, then measurement precision is preserved, but loss of energy and network bandwidth increases
Solution Approach 1:
The patent implements selective video transmission where only essential frames containing recognizable parking objects are transmitted at high resolution. During periods of high network traffic, the system reduces transmission frequency or resolution for non-critical frames while maintaining high quality for frames that contain objects requiring recognition, thus preserving measurement precision when needed while reducing overall bandwidth consumption.
Data Source
AI summary
The technologies regarding dynamic image-based parking systems and methods of operation thereof are disclosed. An example method for operating a camera-based parking management system includes: activating an AI-enabled camera covering a parking area responsive to detecting a parking object; capturing a snapshot of the parking area using the AI-enabled camera; transmitting the snapshot to an edge processor to identify parking object(s) shown in the snapshot; determining that the parking object shown in the snapshot is not capable of being identified with a predefined degree of certainty based on a machine learning model; responsive to the determining: identifying first one or more characteristics about the parking object that potentially reduce identification accuracy; and identifying second one or more characteristics about the snapshot (e.g., imaging angle, orientation, resolution, network speed) that potentially reduce identification accuracy; and automatically adjusting one or more settings of the AI-enabled camera to capture a second snapshot.