Entity Detection via Camera Classification and Feature Identification
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Solution Overview
Problem
Conventional devices for tracking entities in predefined areas, such as homes or businesses, lack accuracy in identifying and monitoring entities, including people and vehicles, and do not provide efficient data management for customer recognition or transaction analysis.
Innovation Solution
A computer-implemented method and system for entity detection using cameras to classify entities, assign identifiers based on type and features, track entity presence, and generate notifications, which includes a processor and memory configured to detect entities, classify their types, and associate transactions with identifiers for monitoring and data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tracking devices are used to monitor entities in predefined areas, then basic presence detection is achieved, but measurement precision and reliability of entity identification are insufficient
Solution Approach 1:
The entity detection process is segmented into distinct stages: detection phase (camera captures entity), classification phase (type identification), and identification phase (feature-based distinction). This segmentation allows each stage to be optimized independently, improving overall measurement precision without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces an intermediary classification system that bridges basic detection and detailed identification. The classification module acts as a mediator that processes detected entities and routes them to appropriate identification procedures, enhancing accuracy while managing system complexity through modular architecture.
2Reliability
If detailed entity classification and feature detection are implemented, then entity distinction capability is improved, but device complexity and processing requirements increase
Solution Approach 1:
The system dynamically adjusts its processing depth based on entity type and context. For example, vehicles may receive license plate recognition while pedestrians receive facial recognition or gait analysis. This dynamic approach ensures high reliability for entity distinction while avoiding unnecessary processing complexity for each detection scenario.
Solution Approach 2:
The patent changes detection parameters based on entity characteristics. Different camera settings, feature extraction algorithms, and identification thresholds are applied depending on the detected entity type, enabling high distinction accuracy while optimizing processing requirements for each specific case.
3Productivity
If comprehensive entity tracking and transaction analysis are performed, then data management capability is improved, but loss of time for processing and analysis increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing entity features during initial detection. Entity templates, characteristic profiles, and baseline data are established in advance, enabling rapid comparison and analysis during subsequent transactions without requiring time-consuming processing at the moment of interaction.
Solution Approach 2:
The patent implements continuous tracking and monitoring that maintains entity information throughout their presence in the predefined area. This continuous action allows the system to accumulate data over time without interruption, improving productivity by eliminating the need to restart processing for each new transaction while managing time loss through efficient data streaming and real-time analysis.
Data Source
AI summary
A computer-implemented method for entity detection is described. In one embodiment, an entity passing through a perimeter of a predefined area is detected via a camera. Upon detecting the entity passing through the perimeter of the predefined area, a type of the entity is classified from an image of the entity captured by the camera. Upon classifying the type of the entity, a feature of the entity is detected from the image of the entity. An identifier is assigned to the entity based on the type and the detected feature of the entity. The identifier distinguishes the entity from another entity of a same type.