Real-Scene Database Generation via Auto-Detection
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Solution Overview
Problem
Current systems for tracking and managing objects in spaces require manual registration and lack automated methods for generating searchable databases of entities and physical events from camera feeds, limiting their effectiveness in real-time monitoring and identification.
Innovation Solution
A system that uses image processing circuitry to auto-detect and auto-register entities and physical events from camera feeds, generating a searchable real-scene database by processing video and audio data, and assigning unique identifiers to entities, while also detecting and recording relationships between them, enabling automated data management and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual registration is used to track and manage objects, then system complexity is reduced, but productivity and automation extent are worsened
Solution Approach 1:
The system enables automatic self-registration of entities through computer vision technology. The database automatically detects, identifies, and registers entities in the real scene without requiring manual intervention, allowing the system to serve itself in terms of data collection and organization
Solution Approach 2:
The patent replaces manual mechanical registration processes with automated computer vision systems. Image processing circuitry and algorithms substitute for human operators, automatically capturing visual data, processing it through recognition algorithms, and storing results in the database without physical manual intervention
2Ease of operation
If manual registration is used for item tracking, then ease of operation is improved, but extent of automation is worsened
Solution Approach 1:
The system performs automatic self-registration of entities through computer vision technology. The database automatically detects, identifies, and registers entities in the real scene without requiring manual intervention, allowing the system to serve itself in terms of data collection and organization
Solution Approach 2:
The system performs preliminary automatic registration of entities before any tracking or management operations are needed. By pre-detecting and storing entity information in the database, the system prepares the groundwork for subsequent automated tracking without requiring manual setup
3Productivity
If automated computer vision systems are implemented, then productivity and automation are improved, but device complexity and measurement precision requirements are worsened
Solution Approach 1:
The patent divides the complex automated registration task into separate functional modules: image capture by cameras, processing by image processing circuitry, analysis by entity detection algorithms, and storage by the database. This segmentation allows each component to be optimized independently while working together to achieve high productivity
Solution Approach 2:
The database system is designed to handle multiple types of entities (people, animals, objects) and various attributes simultaneously through a unified structure. The image processing circuitry performs multiple functions including detection, recognition, and特征 extraction, reducing overall system complexity despite high productivity requirements
4Loss of information
If comprehensive entity attributes are stored in the database, then information completeness is improved, but loss of time for data processing and retrieval is worsened
Solution Approach 1:
The system performs preliminary organization and tagging of entity attributes during the automatic registration phase. By pre-structuring data with metadata, categories, and relationships before retrieval operations, the system minimizes processing time during actual data access while maintaining complete information
Solution Approach 2:
The patent uses temporary processing structures and intermediate data representations that are created, used for indexing purposes, and then discarded. These lightweight temporary structures enable fast retrieval operations without permanently storing redundant data, balancing information completeness with retrieval speed
Data Source
AI summary
This application discloses to methods, circuits, devices, assemblies and systems for generating a searchable real-scene database including records indicating entities and physical events occurring within the real-scene. The said system may include a camera feed interface to receive a camera feed from each of one or more cameras observing the real-scene. It may also include image processing circuitry including a static scene analysis unit to: (a) extract features of entities appearing in a camera feed, (b) recognize entity types of entities appearing in the one or more camera feeds, (c) assign an entity designator to a specific entity appearing in the camera feeds, (d) generate an entity designator record for a uniquely identifiable entity, and generate an attribute record for a uniquely identifiable entity. The system may also include a scene dynamics analysis unit to: (a) detect entity movements, (b) detect entity speech, (c) characterize entity actions based on detected entity movement and/or entity speech, and (d) generate a record indicting an action taken by an entity.