Visual Vehicle Parking Occupancy Sensor Using Cascaded HSV Feature Search
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Solution Overview
Problem
Existing vehicle parking occupancy sensing technologies face challenges in achieving high accuracy and efficiency, particularly with cost-effective solutions that can reliably determine vehicle presence in parking spaces without relying on expensive infrastructure like depth cameras or surface-embedded sensors.
Innovation Solution
A visual vehicle parking occupancy sensor system utilizing a two-dimensional camera with a controller that implements a multi-phased search for vehicle features using HSV feature extraction and classification, refining searches through image processing to increase accuracy and reduce noise, while partitioning the image into regions of interest to focus on relevant pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive infrastructure like depth cameras or surface-embedded sensors is used, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical sensing infrastructure (depth cameras, embedded sensors) with a visual system using standard 2D cameras. The occupancy detection is achieved through image processing and machine learning classification of visual data, substituting mechanical sensing with optical imaging and computational analysis to reduce hardware complexity while maintaining detection accuracy
Solution Approach 2:
The patent creates a visual representation (image) of the parking space and uses machine learning models to infer occupancy status from this copy rather than directly measuring physical occupancy. The system analyzes visual features such as vehicle presence, color, and position to determine occupancy, replacing direct physical sensing with indirect visual inference
2Device complexity
If a two-dimensional camera is used instead of depth cameras, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent transforms the 2D image data into enhanced feature representations by converting to HSV color space and applying machine learning classification. The system changes the parameter representation from raw pixel values to color-segmented features and occupancy probability scores, enabling accurate occupancy detection despite using simple 2D camera hardware
Solution Approach 2:
The patent introduces an intermediary processing layer between the 2D camera and occupancy determination. This intermediary consists of image processing algorithms, color space transformation, and machine learning models that mediate the conversion of simple visual data into accurate occupancy information, bridging the gap between simplified hardware and precise measurement
3Measurement precision
If multi-phased search for vehicle features is implemented, then measurement precision is improved, but use of energy and computing resources increase
Solution Approach 1:
The patent segments the occupancy detection task into multiple phases: initial image capture, color space conversion, feature extraction, machine learning classification, and occupancy determination. Each phase processes only the necessary portion of the data at that stage, reducing overall computational load while maintaining high precision through systematic progression through detection phases
Data Source
AI summary
System and techniques for a visual vehicle parking occupancy sensor are described herein. A color image, including a parking space, is received from a camera. A cascaded search for vehicle features in a hue-saturation-value (HSV) converted version of the color image is performed to produce search results. A search for macro vehicle features in the color image is also performed to produce an indication of found macro vehicle features when the search results are of a first type. An occupancy indicator is provided based on the search results when the search results are of a second type and based on the indication otherwise.