Image Debugging With Anonymous-Object Heatmaps for Space Usage
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Solution Overview
Problem
Existing technologies lack effective methods for debugging images and tracking usage patterns of anonymous objects within a space while maintaining employee privacy and optimizing furniture layout and sensor block positioning.
Innovation Solution
A method involving sensor blocks and a computer system to capture and analyze frames, detect objects, generate heatmaps, and adjust furniture layout and sensor positions based on usage patterns, using low-resolution frames and generic graphical representations to maintain privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution images are captured and analyzed to track object usage patterns, then measurement precision and object detection accuracy are improved, but employee privacy is compromised and data storage requirements increase
Solution Approach 1:
The system extracts only the essential information needed for object usage pattern analysis while removing or anonymizing personally identifiable information. Instead of processing complete high-resolution images containing employee identities, the system extracts object types, locations, and usage patterns while stripping out privacy-sensitive data, thus achieving accurate tracking without compromising employee privacy
Solution Approach 2:
The system applies different processing quality levels to different parts of the image data. Critical regions containing object information are processed with high precision for accurate detection, while regions containing personal information are processed at lower resolution or anonymized, allowing the system to maintain measurement precision for objects while protecting employee privacy in other areas
2Productivity
If comprehensive object tracking and analysis is implemented, then productivity insights and furniture layout optimization are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system segments the comprehensive tracking task into smaller, manageable modules: image capture, object detection, pattern analysis, and optimization recommendation. Each module performs a specific function with defined inputs and outputs, reducing overall system complexity while maintaining comprehensive productivity analysis capabilities through the coordinated operation of these modular components
Solution Approach 2:
The system performs preliminary actions by pre-defining object categories, usage patterns, and analysis parameters before actual tracking begins. This preprocessing of analytical frameworks and categorization schemes simplifies the real-time processing requirements and reduces computational complexity during operation, as the system only needs to match observed objects against pre-established patterns rather than creating analysis frameworks on-the-fly
Data Source
AI summary
One variation of a method includes, at a sensor block: detecting a set of objects within a region-of-interest in a frame; detecting an object type of each object; detecting a location of each object within the region-of-interest; and storing object types and object locations of the set of objects in a set of containers. The method further includes, at the computer system: accessing a database of commissioning images; extracting a commissioning image annotated with boundaries from the database; initializing a visualization layer of a set of pixels representing the region-of-interest; and calculating a frequency of presence of the object type intersecting each pixel based on the set of containers; calculating a color value for each pixel based on the frequency of presence; and assigning the color value to each pixel in the visualization layer; and generating a heatmap of the region-of-interest based on the visualization layer and the commissioning image.