Occupancy Monitoring via Vision Transformer Load Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for monitoring the occupancy of structures rely on counting people, which is inaccurate due to varying weights and do not provide real-time updates, leading to false alarms when determining if the load threshold has been reached.
Innovation Solution
A system utilizing a camera, processor, and deep neural network for real-time image processing to detect and track individuals, calculate the load based on their attributes, and generate an output signal when the predetermined threshold is reached, incorporating a Vision Transformer and Squeeze and Excitation block for accurate and robust monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system counts the number of occupants to determine occupancy limit, then the system complexity is reduced and ease of operation is improved, but the measurement precision deteriorates because individual weights vary greatly
Solution Approach 1:
The patent replaces the simple counting mechanism with a computer vision-based detection system that captures images, processes them through neural networks, and estimates individual weights based on visual attributes. This substitution transitions from a crude mechanical count to a more sophisticated optical measurement system that infers physical properties without direct contact.
Solution Approach 2:
The system changes the measurement parameter from a discrete count (number of people) to a continuous estimation (weight-based load calculation). By extracting visual features such as body size, posture, and clothing from images, the system transforms qualitative visual data into quantitative weight estimates, thereby improving measurement precision while maintaining operational simplicity.
2Device complexity
If the system uses static or non-real-time counts to monitor occupancy, then the device complexity is reduced, but the reliability deteriorates due to false positive or false negative alarms
Solution Approach 1:
The patent implements continuous real-time monitoring by capturing images at regular intervals and continuously updating the occupancy assessment. The system processes a stream of images rather than relying on periodic snapshots, ensuring that the occupancy status is always current and reducing the likelihood of false alarms caused by delayed detection of occupancy changes.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously comparing current occupancy status with historical data and alert thresholds. When the calculated load approaches or exceeds the threshold, the system generates alerts that provide immediate feedback to occupants or monitoring personnel, enabling timely response and preventing false negative situations where overload goes undetected.
3Measurement precision
If the system implements real-time monitoring with individual tracking, then the measurement precision and reliability are improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent segments the image processing task by first detecting individual objects in the image, then extracting features specific to each detected object, and finally estimating weights for each person separately. This segmentation allows the complex processing to be broken down into manageable stages, improving measurement precision through individualized assessment while organizing system complexity into modular components.
Solution Approach 2:
The system applies partial action by focusing computational resources only on detected human figures rather than processing the entire image uniformly. By identifying and tracking only relevant objects (people) and extracting features specifically from them, the system achieves high measurement precision for occupancy assessment without the excessive computational burden of analyzing every pixel or object in the scene.
Data Source
AI summary
A system for monitoring occupancy of a structure includes at least one camera configured to acquire an image of the structure; processor circuitry coupled with a memory configured to process the image; and an output device; wherein, in order to process the image, the processor circuitry is configured to: detect an object in the image; extract one or more features of the object; identify whether the object is a person; associate a unique tracker with each identified person; keep track of the identified people in the structure; calculate a load on the structure based on an attribute of each person; and instruct the output device to generate an output signal when the calculated load reaches a predetermined threshold.