Vision System Occupancy Counting Error Correction
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Solution Overview
Problem
Conventional vision systems for security and occupancy counting often fail to provide accurate object detection and counting due to sub-par algorithms, aging hardware, and various environmental obstacles, leading to inaccuracies in exit and entry counts.
Innovation Solution
The system employs a processor to detect persons using image frames from sensors, calculates error rates based on historical data, and adjusts counts using error rates to provide corrected exit and entry counts, reducing the need for manual input and improving accuracy through supervised and unsupervised machine learning for real-time occupancy counting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional detection algorithms are used, then the system is simple to implement, but the detection accuracy and counting precision deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting historical egress data and calculating error rates in advance. The processor stores historical detected exit counts and corrected exit counts in a database, then uses this pre-computed error rate information to adjust current detection results, improving accuracy without requiring complex real-time processing during actual counting operations.
Solution Approach 2:
The system implements feedback mechanisms by using historical correction data to improve current detection accuracy. The processor retrieves historical egress data, calculates error rates based on discrepancies between detected and actual historical counts, and applies these error rates to correct current exit counts. This feedback loop continuously improves measurement precision while maintaining manageable system complexity.
2Measurement precision
If manual correction of detection errors is implemented, then the accuracy improves, but the operation time and labor requirements increase
Solution Approach 1:
The system performs self-service by automatically correcting detection errors without requiring manual intervention. The processor autonomously retrieves historical egress data, calculates error rates, determines corrected exit counts, and stores the corrected data in the database. This automated self-correction process improves exit count accuracy while eliminating the time loss associated with manual correction operations.
Solution Approach 2:
The system performs preliminary correction actions by pre-calculating and storing error rates based on historical data. When new detection data arrives, the system already has pre-computed correction factors ready to apply, eliminating the need for time-consuming manual analysis and correction of each new dataset.
3Measurement precision
If historical data is collected and processed, then the error rate calculation becomes more accurate, but the data processing complexity increases
Solution Approach 1:
The system segments the data processing task into distinct modular components: data collection module that gathers historical egress data, error rate calculation module that computes correction factors, and data storage module that saves processed information in a database. This segmentation allows each component to handle specific processing tasks independently, improving error rate accuracy while keeping overall system complexity manageable through clear separation of concerns.
Solution Approach 2:
The system introduces an intermediary database that stores historical egress data and calculated error rates. This database acts as a mediator between data collection and correction application processes, allowing accurate error rate calculation based on historical patterns without requiring complex real-time processing. The intermediary database simplifies data flow and reduces processing complexity while maintaining high measurement precision.
Data Source
AI summary
Aspects of the present disclosure relate generally to a vision system that detects objects moving in an environment. The vision system detects objects that crossed a boundary of an environment during a first period of time, and determines a count of objects that crossed the boundary in a first direction. The vision system retrieves historical egress and ingress data for a second period of time corresponding to the first period of time. The historical egress and ingress data comprises a detected historic count of crossings in the first direction and a corrected historic count of crossings in the first direction. The vision system calculates an error rate for the second period of time based on a ratio of the detected historic count and the corrected historic count, and determines a corrected count for the first period of time by adjusting the count using the error rate.