Clustering-Based Occupancy Detection Without Labeled Training Data
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Solution Overview
Problem
Existing occupancy detection technologies, such as YOLO-based methods, require high-performance hardware and labeled learning data, making them inefficient and costly, and existing sensors like PIR and ultrasonic sensors have limitations in accuracy and resource constraints.
Innovation Solution
A clustering-based occupancy detection device and method using unsupervised learning techniques, including a ToF camera, preprocessing, clustering, and tracking units to accurately detect occupants without labeled data, utilizing a mean shift algorithm and Gaussian functions for clustering and bounding boxes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If YOLO-based occupancy detection is used, then detection accuracy is improved, but hardware resource requirements and data preparation costs increase
Solution Approach 1:
The patent replaces complex deep learning models (YOLO) with simpler clustering algorithms (K-means, DBSCAN, Mean Shift) that can be implemented on low-power hardware. This substitution maintains acceptable detection accuracy while dramatically reducing computational resource requirements and enabling deployment on edge devices without high-performance GPUs.
Solution Approach 2:
The patent uses lightweight, computationally inexpensive clustering algorithms that can be executed efficiently on resource-constrained devices. These algorithms process image data through simple iterative computations rather than requiring the heavy matrix operations of deep learning models, thereby reducing hardware complexity and energy consumption.
2Measurement precision
If YOLO-based occupancy detection is used, then detection accuracy is improved, but time and cost for labeled learning data increase
Solution Approach 1:
The patent employs unsupervised learning clustering algorithms that automatically identify occupancy patterns without requiring pre-labeled training data. The algorithms self-organize pixel or region data into clusters based on spatial and temporal characteristics, eliminating the time-consuming process of manual annotation and reducing the need for curated datasets.
Solution Approach 2:
The patent extracts and utilizes only the essential features from image data (such as spatial position, temporal presence, and motion patterns) to perform clustering, rather than requiring comprehensive labeled datasets. This extraction approach focuses on the minimum necessary information to achieve accurate occupancy detection, thereby reducing data preparation requirements.
3Device complexity
If PIR sensors are used, then device simplicity is maintained, but detection accuracy deteriorates when there is no movement
Solution Approach 1:
The patent merges the advantages of simple sensors with the power of computational clustering algorithms. By combining basic sensor inputs (image frames from cameras or data from simple detectors) with unsupervised learning processing, the system achieves both simplicity in data collection and high accuracy in occupancy detection, overcoming the limitations of traditional PIR sensors.
Solution Approach 2:
The patent changes the detection parameters from movement-based (PIR) to presence-based clustering analysis. Instead of relying on motion detection that fails when occupants are stationary, the system analyzes spatial and temporal parameters through clustering to detect occupancy presence, thereby maintaining accuracy in static scenarios while keeping the overall system simple.
4Reliability
If ultrasonic sensors are used, then occupancy detection is achieved, but measurement precision is reduced due to signal distortion
Solution Approach 1:
The patent substitutes ultrasonic wave-based detection with image-based clustering analysis. By replacing acoustic sensing with visual or spatial data processing, the system eliminates signal distortion issues inherent in ultrasonic waves interacting with objects and walls, thereby improving measurement precision while maintaining reliable occupancy detection capability.
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
Enables efficient and accurate occupancy detection in low-power environments by reducing resource requirements and data preparation time, maintaining high reliability through flexible obstacle removal and clustering adjustments.
Implementation Method 1
an input module configured to collect image data captured by a camera; In the present invention, the input module may collect image data captured by a time of flight (ToF) camera installed at an entrance
Data Source
AI summary
The present invention relates to a clustering-based occupancy detection device which includes: an input module configured to collect image data captured by a camera; a preprocessing unit configured to perform a preprocessing operation for removing unnecessary information from the image data collected by the input module so that only occupants remain; and a clustering unit configured to detect positions of the occupants based on the image data preprocessed by the preprocessing unit.