Event-Driven Parking Occupancy Detection System
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Solution Overview
Problem
Current methods for determining parking occupancy at rest stops, especially for truck drivers, are either manual and inefficient or automated but prone to errors and high costs due to continuous video monitoring, which consumes significant resources and leads to inaccuracies in space classification.
Innovation Solution
An event-driven system combining ingress/egress sensors with video cameras to analyze images only when a triggering event occurs, significantly increasing accuracy by calculating 'by spot occupancy' using regions of interest and reducing unnecessary processing and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If continuous video monitoring is used to determine parking occupancy, then occupancy detection is automated, but resource consumption and cost increase significantly
Solution Approach 1:
The system transitions from continuous video monitoring to periodic image capture triggered by specific events (vehicle entry/exit). The controller captures images only when a vehicle enters or exits the parking lot, rather than continuously monitoring all parking spaces. This periodic action based on triggering events significantly reduces resource consumption while maintaining automated occupancy detection.
2Extent of automation
If continuous video monitoring is used to determine parking occupancy, then occupancy detection is automated, but errors accumulate over time
Solution Approach 1:
The system extracts only the essential information needed for occupancy detection by capturing specific images at triggering events rather than continuously monitoring. By taking out only the necessary data points (images at entry/exit events) and processing them through image analysis to determine occupancy changes, the system reduces error accumulation while maintaining automation.
3Use of energy by moving object
If manual monitoring is used to determine parking occupancy, then resource consumption is reduced, but efficiency and accuracy decrease
Solution Approach 1:
The system enables self-service automated occupancy detection by using the existing parking lot infrastructure (entry/exit points) as triggering mechanisms. The controller automatically captures images and analyzes occupancy changes without requiring manual intervention, while consuming minimal resources compared to continuous monitoring. The parking lot itself provides the triggering events that drive the automated detection process.
4Use of energy by moving object
If manual monitoring is used to determine parking occupancy, then cost is reduced, but measurement precision and reliability decrease
Solution Approach 1:
The system replaces manual mechanical monitoring with automated image-based detection. The controller captures images and uses image analysis to automatically determine occupancy changes, substituting human visual inspection with automated optical measurement. This substitution improves measurement precision and reliability while keeping costs low by only activating at triggering events rather than continuous operation.
Data Source
AI summary
A method, non-transitory computer readable medium and apparatus for calculating a by spot occupancy of a parking lot are disclosed. For example, the method includes receiving an indication of a triggering event, sending a query to receive a first image and a second image in response to the triggering event, receiving the first image and the second image, analyzing the first image and the second image to determine a change in an occupancy status of a parking spot within the parking lot and calculating the by spot occupancy of the parking lot based on the change in the occupancy status of the parking spot.