Weighted Sensor Fusion for Occupancy Counting
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Solution Overview
Problem
Existing occupancy monitoring systems using LIDAR sensors are prone to inaccuracies due to power loss and video cameras raise privacy concerns, while computer vision systems may fail to detect all individuals, necessitating a more accurate and privacy-respecting method for counting people in areas.
Innovation Solution
A system utilizing multiple sensors, including LIDAR and non-invasive imaging technologies like ultra-wide band radar and thermal sensors, combines counts with weighted accuracy based on sensor confidence to provide a more accurate occupancy count.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LIDAR sensor is used for occupancy monitoring, then occupancy count can be obtained, but accuracy deteriorates due to power loss causing count reset
Solution Approach 1:
The patent combines LIDAR sensor data with data from alternative sensors (cameras, thermal sensors, radar) to create a fused occupancy count. This merging compensates for LIDAR's vulnerability to power loss by incorporating data from sensors that maintain operation during power fluctuations, thereby resolving the contradiction between reliability and measurement precision.
Solution Approach 2:
The system implements feedback mechanisms where occupancy counts from multiple sensors are continuously compared and adjusted. When LIDAR count resets occur, the system uses feedback from other sensors to detect discrepancies and correct the occupancy count, maintaining accuracy despite power loss events.
2Measurement precision
If video camera with computer vision is used for occupancy monitoring, then occupancy count can be obtained, but privacy concerns arise due to ability to obtain personal identifying information
Solution Approach 1:
The patent extracts only the necessary occupancy count information from video data while deliberately excluding personal identifying information. By processing video feeds to count people without capturing or storing identifiable features, the system maintains measurement precision while eliminating privacy violations.
Solution Approach 2:
The system uses intermediary processing steps that convert raw video data into anonymized occupancy counts. This intermediary layer processes visual information to extract numerical occupancy data while filtering out personal identifying information, thus resolving the contradiction between accurate counting and privacy protection.
3Device complexity
If single sensor type is used for occupancy monitoring, then system complexity is reduced, but measurement precision deteriorates due to sensor limitations
Solution Approach 1:
The patent segments the occupancy monitoring function across multiple sensor types, with each sensor contributing to specific aspects of detection. This segmentation allows the system to maintain relatively simple individual sensor components while achieving high overall measurement precision through their combined operation.
Solution Approach 2:
The system implements multi-functionality by using a suite of sensors that can each perform occupancy detection independently. This universal approach allows any single sensor to provide basic functionality while the combination delivers enhanced precision, balancing device complexity with measurement accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances accuracy in occupancy monitoring, enabling better control of automation systems and emergency response, while respecting privacy by using non-invasive sensors.
Implementation Method 1
Some such systems use a light and detection ranging (LIDAR) sensor to detect when people enter and/or exit the area
Implementation Method 2
A first count of people in the area is obtained, based on first data from a LIDAR sensor... A second count of people in the area is obtained, based on second data from a thermal sensor
Implementation Method 3
A second count of people in the area is obtained, based on second data from an ultra-wide band radar sensor
Data Source
AI summary
Systems and methods are disclosed for computing an occupancy count of an area based on data from multiple sensors. A first count of people in the area at a time instance can be obtained based on first data from a first sensor monitoring an entry point to the area, a second count of people in the area at the time instance can be obtained based on second data captured by a second sensor of a sensor type other than the first sensor, and the occupancy count of the area at the time instance can be computed as a sum of the first count, which may have a first weight applied, and the second count, which may have a second weight applied.