Visual Sensor Behavior Recognition in Spatial Zones
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Solution Overview
Problem
Existing sensor-based environments struggle to provide a personalized and adaptive experience for individuals by accurately interpreting behaviors within a spatial and personal context, leading to inefficiencies in transactions and user satisfaction.
Innovation Solution
A method is introduced that uses visual sensors to acquire image information, determine location, and identify behaviors, accessing predefined relationship information to perform specific actions based on the behavior and location within predefined regions, enabling customizable and adaptive experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor-based environments use basic sensor detection without contextual analysis, then device complexity is reduced, but behavior interpretation accuracy deteriorates
Solution Approach 1:
The system segments behavior interpretation into distinct components: detection (visual sensors), spatial context determination (location within predefined regions), and behavioral analysis (pattern recognition). This segmentation allows each component to be optimized independently while maintaining overall system manageability.
Solution Approach 2:
The patent introduces an intermediary layer of spatial and personal context that mediates between raw sensor data and behavior interpretation. This intermediary context layer enhances accuracy without requiring direct complex analysis of all sensor inputs, thereby managing system complexity.
2Adaptability or versatility
If the system implements personalized and adaptive experience interpretation, then user satisfaction improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-defining regions, behaviors, and relationship information before actual behavior interpretation occurs. This preparation allows the system to quickly match detected behaviors against predefined patterns, reducing processing time during actual operation while maintaining personalization capabilities.
Solution Approach 2:
The system changes parameters by adjusting the level of personalization and adaptation based on available information. When sufficient contextual data exists, the system applies personalized interpretation; when data is limited, it falls back to general behavior recognition, thereby balancing processing time and personalization.
3Measurement precision
If the system analyzes behaviors without spatial context, then measurement simplicity is maintained, but behavior detection accuracy deteriorates
Solution Approach 1:
The patent applies local quality by associating specific behaviors with specific spatial regions. Different regions have predefined behaviors relevant to their function, allowing the system to interpret behaviors accurately based on location without requiring complex global analysis of all spatial relationships.
Data Source
AI summary
Method, computer program product, and system for use with an environment divided into a plurality of predefined regions. The method comprises acquiring first image information including a first person, determining location information for the first person, and identifying a first behavior of the first person from a plurality of predefined behaviors. The method further comprises performing a first predefined action responsive to identifying the first behavior. Performing the first predefined action comprises determining a first region of the plurality of predefined regions corresponding to the location information, and accessing a memory storing predefined relationship information between the plurality of predefined behaviors and the plurality of predefined regions. The predefined relationship information comprises a plurality of predefined actions. Performing the first predefined action further comprises selecting, using at least the first behavior and the first region, the first predefined action from the plurality of predefined actions.