Workplace Space Mapping for Privacy-Preserving Anomaly Detection
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Solution Overview
Problem
Existing workplace monitoring systems lack the ability to efficiently detect and respond to conditions within a space, particularly in identifying anomalous object clusters and maintaining privacy while providing actionable insights.
Innovation Solution
A method utilizing a population of sensor blocks to capture images, compile object lists into a map, and detect deviations from nominal conditions, generating notifications for anomalous conditions while maintaining privacy through generic graphical representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed object identification is performed to provide actionable insights, then monitoring precision is improved, but personal privacy is compromised
Solution Approach 1:
The system creates a simplified copy or representation of objects in the workspace rather than processing detailed identifying information. Generic object representations are used for analysis while actual personal data is excluded, allowing monitoring functions to operate without compromising privacy.
Solution Approach 2:
The system extracts only the necessary spatial and relational information from images while removing or excluding personally identifiable features. This selective extraction allows the system to analyze object locations and relationships without capturing or storing personal privacy information.
2Speed
If real-time monitoring is implemented to detect anomalies quickly, then response speed is improved, but system complexity increases
Solution Approach 1:
The system pre-establishes nominal conditions and object relationships before actual monitoring occurs. By defining expected states in advance, the system can quickly compare real-time data against these pre-set criteria to rapidly detect anomalies without complex real-time analysis.
Solution Approach 2:
The monitoring system is divided into separate functional modules: image capture, object detection, relationship analysis, and anomaly detection. Each module operates independently and processes specific tasks, reducing overall system complexity while maintaining real-time capability through parallel processing.
3Measurement precision
If comprehensive object detection is performed to identify all objects, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system applies different detection strategies to different spatial regions and object types based on their importance. High-priority areas and critical objects receive more detailed analysis, while less important regions are processed with simpler methods, optimizing the balance between detection precision and processing time.
Solution Approach 2:
The system performs partial object detection focused on identifying objects and their relationships rather than analyzing every detail of every object. This selective approach provides sufficient precision for anomaly detection without the time cost of exhaustive analysis.
Data Source
AI summary
A method for detecting conditions within a space includes: accessing a corpus of object lists generated based on objects detected in images captured by a population of sensor blocks; compiling locations of a set of objects represented in the corpus of object lists into a map of the space based on known locations of the population of sensor blocks; accessing a nominal condition of the space defining a set of inclusion objects and a set of exclusion objects within a threshold distance of an anchor object type; and detecting the anchor object type in the map according to the nominal condition. The method further includes, in response to detecting an object within the threshold distance of the anchor object type in the map and deviating from the nominal condition: identifying the object as anomalous in the map; and generating a notification to investigate the object.