Vision-Based Parking Occupancy Detection via Normalized View Classifiers
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Solution Overview
Problem
Current camera-based systems for estimating parking occupancy are limited by high installation and maintenance costs, and existing methods require site-specific training, making them impractical for temporary deployments and multiple sites without substantial return on investment.
Innovation Solution
A camera-based system that uses a mobile vehicle-detection device capable of deploying in various parking areas, employing a normalized geometric space transformation and pre-trained global classifiers to estimate occupancy without extensive site-specific training, allowing for rapid data collection across different sites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor-based solutions are used for monitoring parking spaces, then occupancy detection accuracy is improved, but installation and maintenance costs increase significantly
Solution Approach 1:
The patent replaces physical sensor-based detection systems with a vision-based system using cameras and image processing algorithms. The camera captures images of parking spaces, and computer vision techniques analyze these images to detect vehicle occupancy, eliminating the need for expensive physical sensors while maintaining detection accuracy
Solution Approach 2:
The patent creates a visual copy or representation of the physical parking environment through camera imaging. Instead of using physical sensors in each parking space, the system uses optical copies (images) of the parking areas to extract occupancy information, reducing hardware costs while preserving measurement capabilities
2Measurement precision
If existing camera monitoring systems are used with site-specific training, then detection accuracy is improved, but deployment time and complexity increase for temporary operations
Solution Approach 1:
The patent develops a universal classifier model that can detect vehicles across multiple different parking environments without requiring site-specific training. The normalized-view approach creates a common representation format that makes the detection system applicable to various parking areas, streets, and configurations using the same trained model, enabling rapid deployment to temporary locations
Solution Approach 2:
The patent performs classifier training in advance using a diverse dataset of parking images from multiple locations and conditions. This preliminary training creates a pre-trained model that is ready for immediate deployment without requiring on-site data collection or training, allowing the system to be rapidly deployed to temporary parking areas
3Adaptability or versatility
If a mobile system is deployed to multiple sites temporarily, then adaptability and scalability are improved, but measurement accuracy deteriorates without site-specific training
Solution Approach 1:
The patent applies geometric normalization transformations to change the spatial parameters of detected objects in images. By normalizing the view and spatial representation of vehicles across different camera angles and distances, the system creates a consistent parameter space that allows a single classifier to accurately detect vehicles in diverse parking environments without site-specific adaptation
Solution Approach 2:
The patent transforms the detection problem from direct image space analysis to a normalized geometric space. By introducing a normalized dimension where all vehicles are represented in a common coordinate system regardless of their original position or scale in the image, the system achieves both multi-site adaptability and maintained detection accuracy
Data Source
AI summary
A system for estimating parking occupancy includes a vehicle-detection device including an adjustable mast supporting an image capture device at a select height. The image capture device acquires video of a current parking area. A computer processor in communication with the image capture device is configured to receive the video data and define a region of interest in the video data. The processor is further configured to perform a spatial transform on the ROI to transform the ROI to a normalized geometric space. The processor is further configured to apply features of a detected object in the normalized geometric space to a vehicle classifier—previously trained with samples acquired from a normalized camera perspective similar to the normalized geometric space—and determine the occupancy of the current parking area using an output of the classifier.