Spatial Object Visualization Using Anchor-Based Deviation Detection
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Solution Overview
Problem
Existing technologies lack effective methods for accurately detecting and visualizing objects within a space, particularly in workplace environments, to monitor furniture layout and usage, and to identify deviations in real-time.
Innovation Solution
A method involving sensor blocks with optical and motion sensors, computer vision techniques, and machine learning models to detect and differentiate between anchor and mutable objects, projecting images onto a digital map, and generating real-time visualizations with notifications for deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive object detection and visualization is implemented, then measurement precision and reliability are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments objects into anchor objects (static, unmovable) and mutable objects (dynamic, movable), allowing differentiated detection and tracking strategies. This segmentation enables the system to process only relevant changes (mutable objects) while maintaining stable reference frames (anchor objects), reducing computational complexity while preserving detection accuracy.
Solution Approach 2:
The system introduces a visualization layer as an intermediary between raw sensor data and final object detection results. This layer projects sensor block images onto a virtual map, serving as a mediator that organizes and contextualizes object information before final detection and tracking occurs, thereby managing system complexity.
2Productivity
If real-time object tracking and deviation detection is implemented, then productivity and responsiveness are improved, but use of energy increases
Solution Approach 1:
The system employs periodic action by tracking only mutable objects that can change position over time, rather than continuously processing all detected objects. The system updates object locations and detects deviations at discrete time intervals, maintaining real-time monitoring capability while reducing energy consumption compared to continuous processing.
Solution Approach 2:
The system dynamically adjusts its monitoring focus by distinguishing between static anchor objects and dynamic mutable objects. Energy resources are allocated dynamically to track only the mutable objects that require monitoring, rather than uniformly processing all objects, thereby improving productivity while managing energy consumption.
3Measurement precision
If detailed object location and deviation analysis is implemented, then measurement precision is improved, but loss of information increases due to data processing requirements
Solution Approach 1:
The system extracts only the necessary information for deviation detection by focusing on changes in mutable object locations relative to anchor objects. Rather than processing and retaining all raw sensor data, the system extracts and stores only the critical deviation information, maintaining location accuracy for relevant objects while minimizing data processing requirements and information loss.
Data Source
AI summary
One variation of a method for detecting and visualizing objects within a space includes: retrieving a map of the space annotated with known locations of target anchor objects and regions in the space; accessing a set of images annotated with object types and locations of objects captured by a set of sensor blocks; projecting the set of images onto the map to form a visualization representing objects in the space based on known locations of the set of sensor blocks; isolating a target anchor object at a known location in a region of the visualization; detecting a mutable object at a location in the region; calculating an offset distance between the location and the known location; and, in response to the offset distance exceeding an offset distance threshold, highlighting the mutable object in the visualization as a deviation; and generating a notification to investigate the mutable object in the region.