Entity Interaction Recognition via 3D Spatial Layout and Proxemics
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Solution Overview
Problem
Current technologies face challenges in automatically detecting and classifying higher-level interactions between entities, such as people and objects, in digital images, due to factors like occlusion, pose, shape, and appearance variations, making it difficult to recognize scenes and perform generic category recognition effectively.
Innovation Solution
An image classification system that uses computer-implemented algorithms to determine a 3D layout of entities in images, applying automated reasoning and AI methods to infer interactions, and includes a module for perspective rectification and classification, utilizing proxemics-based analysis to classify human interactions without manual tagging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated algorithms are used to detect and classify entity interactions, then productivity is improved, but measurement precision deteriorates due to occlusion, pose, shape, and appearance variations
Solution Approach 1:
The system segments the image processing task into multiple stages: detecting individual entities first, then determining their spatial configurations, and finally classifying interactions based on proxemics attributes. This segmentation allows each stage to be optimized independently, improving overall accuracy while maintaining automated processing speed.
Solution Approach 2:
The patent introduces proxemics-based analysis as an intermediary layer between entity detection and interaction classification. This intermediary uses spatial configuration attributes (distance, orientation, relative position) as mediators to bridge the gap between low-level visual features and high-level interaction semantics, improving measurement precision without sacrificing productivity.
2Measurement precision
If manual tagging is used for image classification, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system enables self-service classification by automatically computing proxemics attributes from detected entity positions and using these attributes to classify interactions without human intervention. The algorithm serves itself by generating both the spatial measurements and the classification labels, eliminating manual tagging time while maintaining precision through systematic analysis.
Solution Approach 2:
The system performs preliminary detection of entities and computation of their spatial configurations before classification. By pre-computing proxemics attributes such as distance, orientation, and relative position, the system prepares the necessary information in advance, enabling rapid and accurate classification without manual tagging.
3Adaptability or versatility
If generic category recognition is attempted, then adaptability is improved, but difficulty of detecting and measuring increases due to scene complexity
Solution Approach 1:
The patent applies local quality by focusing detection and measurement efforts on specific proxemics attributes relevant to each interaction type rather than analyzing the entire scene globally. By concentrating on local spatial relationships (distance, orientation, position) between entities, the system reduces detection difficulty while maintaining adaptability to recognize various interaction categories.
Solution Approach 2:
The system simplifies detection by changing parameters from complex semantic analysis to measurable geometric attributes. By transforming the problem into detecting and measuring concrete parameters like inter-entity distance, relative orientation, and spatial position, the system reduces detection difficulty while maintaining versatility in recognizing different interaction types through these standardized parameters.
Data Source
AI summary
An entity interaction recognition system algorithmically recognizes a variety of different types of entity interactions that may be captured in two-dimensional images. In some embodiments, the system estimates the three-dimensional spatial configuration or arrangement of entities depicted in the image. In some embodiments, the system applies a proxemics-based analysis to determine an interaction type. In some embodiments, the system infers, from a characteristic of an entity detected in an image, an area or entity of interest in the image.