Presence Detection Using Clustered Pixel Configuration
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Solution Overview
Problem
Current presence detection systems, such as those using image sensors, face challenges in achieving accurate spatial resolution, leading to false positives and negatives, which affect energy efficiency and user safety, particularly due to the full-image processing requirements and sensitivity to external factors like daylight fluctuations.
Innovation Solution
A presence detection system utilizing a camera module with an image sensor and signal processor that divides the image into clusters, adjusts configuration parameters based on expected presence regions, and weights pixel contributions to improve spatial resolution, reducing computational effort and enhancing reaction speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-image processing is used to detect presence, then detection coverage is comprehensive, but spatial resolution deteriorates and false positives/negatives increase
Solution Approach 1:
The image sensor is divided into multiple clusters of pixels, where each cluster is independently configured to monitor specific regions of interest. This segmentation allows the system to focus computational resources on critical areas, improving spatial resolution and detection accuracy without requiring full-image processing, thereby reducing false positives and negatives.
Solution Approach 2:
Different configuration parameters are applied to different pixel clusters based on their specific monitoring requirements. Regions with higher importance for presence detection receive clusters with enhanced sensitivity and resolution, while less critical regions use standard configurations. This local optimization improves overall detection reliability without uniformly increasing system complexity.
2Measurement precision
If image sensor settings are adjusted to improve detection sensitivity, then presence detection accuracy improves, but computational effort increases
Solution Approach 1:
By segmenting the image sensor into clusters, the system processes smaller, localized regions rather than the entire image. This reduces the computational burden per cluster while maintaining high detection accuracy through focused processing. The signal processor only needs to analyze changes within each cluster's region of interest, significantly lowering overall computational requirements.
Solution Approach 2:
The system applies enhanced processing and sensitivity adjustments only to specific pixel clusters that monitor critical regions, rather than uniformly processing the entire image. This partial action approach achieves high detection accuracy where needed while avoiding unnecessary computational effort in less important areas, optimizing the balance between accuracy and complexity.
3Adaptability or versatility
If uniform configuration parameters are applied to all pixels, then system simplicity is maintained, but spatial resolution and adaptive capability deteriorate
Solution Approach 1:
The system assigns different configuration parameters to different pixel clusters based on their specific monitoring needs and regions of interest. This allows each cluster to be optimized for its local requirements, achieving high spatial resolution and adaptability. The complexity is managed by organizing configurations at the cluster level rather than individual pixel level, maintaining system manageability.
Solution Approach 2:
By dividing the image sensor into manageable clusters, the system can independently configure each cluster for its specific monitoring task. This segmentation enables tailored configuration parameters for different regions without requiring complex individual pixel configuration, achieving high adaptability while keeping the overall system structure organized and manageable.
Data Source
AI summary
A presence detection system (10) is disclosed comprising a camera module (20) comprising an image sensor (21) having a plurality of pixels (22) for capturing an image of a space (1) and a signal processor (23) arranged to process signals from each pixels in accordance with one or more configuration parameters for said pixel; and generate a setting for the image sensor from the processed signals. The system further comprises a controller (30) communicatively coupled to the signal processor and arranged to provide the signal processor with configuration parameters for the pixels based on an expected presence in a region of said image such that the pixels corresponding to said region have at least one different configuration parameter to pixels outside said region; periodically receive the setting from the signal processor; and detect a change in said presence in the space from a change in the received setting. A lighting system including such a presence detection system and a presence detection method are also disclosed.