Person Extraction and Action Analysis for Crowd Identification
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Solution Overview
Problem
In crowded areas like airports, stations, and event venues, it is difficult to identify a specific person of interest among numerous individuals, making it challenging to detect potential criminals or nuisances effectively.
Innovation Solution
An information processing device that captures images, extracts and analyzes person attributes and actions, and identifies specific groups or individuals based on predefined criteria, enabling the detection of suspicious behavior or correlations between persons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If authentication-based identification is used to detect suspicious persons, then reliability of identification is improved, but it becomes impossible to identify desired persons in crowded places with many unspecified persons
Solution Approach 1:
The identification system is segmented into multiple independent modules: person extraction module, attribute extraction module, action extraction module, and identification module. Each module processes specific aspects independently, allowing the system to handle crowded scenes by focusing on local interactions rather than requiring global authentication of all persons.
Solution Approach 2:
The system performs preliminary extraction of person attributes and actions before final identification. By pre-extracting relevant features such as movement patterns, spatial relationships, and behavioral attributes, the system prepares data in advance that enables reliable identification even in crowded environments without requiring prior authentication of all individuals.
2Difficulty of detecting and measuring
If comprehensive person monitoring is implemented in crowded areas, then detection capability is improved, but device complexity increases
Solution Approach 1:
The system extracts only the essential and relevant features from complex crowded scene data, specifically focusing on person attributes (such as clothing, posture) and actions (such as movement patterns, spatial relationships). This extraction approach enables effective detection without requiring complex processing of all visual information, thereby reducing system complexity while maintaining detection capability.
Solution Approach 2:
The system implements partial monitoring by focusing on specific regions of interest and specific behavioral patterns rather than attempting to monitor all persons comprehensively. By concentrating computational resources on extracting and analyzing only relevant actions and attributes in specific areas, the system achieves effective detection capability while avoiding the complexity of complete comprehensive monitoring.
Data Source
AI summary
An information processing device according to the present invention includes a person extraction means that extracts a person in a captured image, an action extraction means that extracts an action of a person group including a plurality of persons other than a given person in the captured image, and an identification means that identifies a given person group based on a result of extraction of the action of the person group.