Occupancy Count Correction via Beta-Binomial Regression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional vision systems for security and occupancy counting often fail to provide accurate object detection and counting due to sub-par detection algorithms, aging hardware, and various environmental obstacles, leading to inaccuracies in entry and exit counts.
Innovation Solution
The proposed solution involves a vision system that uses sensors and computer vision techniques to detect objects, determines entry and exit counts, and employs a probabilistic approach by fitting historical data to a probability distribution to correct exit counts using the beta-binomial regression algorithm, and for entry counts, utilizes a Zero-Inflated Poisson model to improve accuracy in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional detection algorithms and sensors are used, then the system can detect objects in real-time, but the accuracy of entry and exit counts deteriorates due to sensor noise, hardware aging, and environmental obstacles
Solution Approach 1:
The system uses historical ingress and egress data to create probability distributions that provide feedback for correcting real-time detection counts. The beta-binomial distribution models exit probabilities based on historical patterns, while the Zero-Inflated Poisson model handles entry counts, allowing the system to continuously improve accuracy by comparing real-time sensor data against learned historical behaviors and adjusting counts accordingly
Solution Approach 2:
The patent transforms raw sensor detection data into corrected occupancy counts by changing the statistical parameters used for analysis. It applies probability distribution functions (beta-binomial for exits, Zero-Inflated Poisson for entries) that transform noisy sensor readings into reliable count estimates by modeling the statistical behavior of entry and exit patterns over time
2Measurement precision
If the system uses simple counting methods, then the device complexity is low, but the accuracy deteriorates due to inability to correct for missed detections and false positives
Solution Approach 1:
The patent introduces probability distribution models as intermediary layers between raw sensor detection and final occupancy counts. The beta-binomial distribution acts as an intermediary for exit counting, modeling the probability of exits based on historical data. Similarly, the Zero-Inflated Poisson model serves as an intermediary for entry counting, handling the statistical complexity of entry patterns without requiring complex real-time analysis
3Reliability
If the system collects and processes historical data, then the accuracy of count corrections improves, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing historical ingress and egress data in databases. This historical data is pre-processed and stored in formats ready for probability distribution fitting, so that when real-time correction is needed, the system can quickly apply the beta-binomial and Zero-Inflated Poisson models without extensive real-time computation, as the heavy lifting of data collection and initial processing has already been completed
Data Source
AI summary
Disclosed herein are apparatuses and methods for object detection in an environment. An implementation may comprise detecting, using at least one sensor, persons that entered and exited the environment during a first period of time and determining an entry count and an exit count accordingly. The implementation may comprise retrieving, from a database, historical ingress and egress data comprising entry and exit counts of the environment for a second period of time. The implementation may comprise determining an amount of expected exit counts for the first period of time based on the historical ingress and egress data of the second period of time by fitting the historical ingress and egress data to a first probability distribution. The implementation may comprise updating the exit count for the first period of time to the amount of expected exit counts and storing the updated exit count in the database.