Depth Sensor Occupancy Detection Using Greedy Bipartite Matching
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Solution Overview
Problem
Current occupancy estimation solutions are inefficient and invasive, failing to accurately detect and track human presence in real-time due to limitations in existing technologies such as break-beams sensors, ultrasonic sensors, and RGB cameras, which lead to energy waste and safety concerns.
Innovation Solution
A depth sensing system utilizing a depth sensor and an occupancy identification module that tracks and identifies occupants by extracting features like head radius, shoulder size, and height, employing algorithms like greedy bipartite matching, Naïve Bayes, Multilayer Perceptron, or Random Forest to verify and count individuals, integrated with a communication interface for external device connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If break-beams sensors are used for occupancy detection, then detection capability is provided, but they fail to work when multiple people enter or leave through a door simultaneously
Solution Approach 1:
The system segments the occupancy detection task into multiple independent depth map analyses. Instead of treating occupancy as a single binary state, the system creates depth maps for each time frame and identifies individual head positions separately, allowing simultaneous detection of multiple people entering or leaving through doors.
Solution Approach 2:
The system transitions from 2D camera images to 3D depth maps for occupancy detection. By using depth information from depth cameras, the system can distinguish between people at different spatial positions and depths, enabling accurate detection of multiple simultaneous entries that 2D sensors cannot differentiate.
2Reliability
If ultrasonic sensors are used for occupancy detection, then detection capability is provided, but they require significant training and are not pet friendly
Solution Approach 1:
The system replaces ultrasonic acoustic sensors with optical depth sensing technology. Depth cameras capture spatial information using light reflection and time-of-flight measurements, eliminating the need for acoustic training and avoiding disturbance to pets while maintaining reliable occupancy detection capabilities.
3Reliability
If RGB cameras are used for occupancy detection, then visual detection is provided, but they are too privacy invasive and don't work in the dark
Solution Approach 1:
The system uses only depth information from depth maps rather than full RGB images for occupancy detection. By processing only spatial distance data without capturing visual appearance, the system maintains detection reliability while significantly reducing privacy concerns associated with invasive camera surveillance.
Solution Approach 2:
The system changes the detection parameter from visual spectrum (RGB) to depth spectrum (distance measurement). Depth cameras operate independently of visible light conditions, enabling reliable occupancy detection in dark environments without requiring additional illumination that would compromise privacy.
4Reliability
If low resolution thermal imagers are used for occupancy detection, then detection capability is provided, but they do not have good enough resolution to detect multiple people entering/exiting simultaneously
Solution Approach 1:
The system uses depth cameras that provide high-resolution spatial mapping in three dimensions, rather than relying on low-resolution thermal imaging. The depth maps capture precise positional information for multiple individuals simultaneously, enabling accurate detection of simultaneous entries with much higher measurement precision than thermal sensors.
5Measurement precision
If high resolution thermal imagers are used for occupancy detection, then resolution is improved, but they are very expensive
Solution Approach 1:
The system uses commercially available depth cameras that provide high-resolution depth mapping at significantly lower cost than high-resolution thermal imagers. These depth sensors offer sufficient precision for occupancy detection without the prohibitive expense of specialized thermal imaging equipment.
Solution Approach 2:
The system replaces expensive thermal imaging technology with more affordable depth sensing technology. Depth cameras using time-of-flight or structured light methods provide comparable or superior spatial resolution for occupancy detection at a fraction of the cost of high-resolution thermal imagers.
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
The system provides reliable and efficient real-time occupancy estimation, reducing energy waste and enhancing safety by accurately tracking and identifying individuals, while being non-invasive and capable of functioning in various environments, including those with multiple occupants and varying lighting conditions.
Implementation Method 1
The depth sensor may be a Time of Flight (TOF) sensor
Data Source
Figure 1A~1B
Figure 1C
Figure 2
AI summary
A depth sensing based system for performing at least one of detecting, tracking, estimating, or identifying occupancy in real-time includes a processor, a computer readable media, and a communication interface communicatively coupled to each other via a system bus. An occupancy estimation module is integrated into the system. A greedy bipartite matching algorithm representing a Fine Grained Occupancy Estimation module (FORK) by leveraging the position, height, and head radius of people is also integrated into the system.