Object Re-Identification via Edge Vector Codes
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Solution Overview
Problem
Existing surveillance systems face challenges in analyzing and aggregating information about objects as they move across multiple camera views due to operational constraints, such as limited fields of view, leading to difficulties in continuous tracking and analytics across discontinuous camera views.
Innovation Solution
A method involving feature extraction and vector code representation of objects by client edge devices connected to cameras, with a local cluster server aggregating and comparing these representations to re-identify objects across multiple camera views, allowing for continuous tracking and data aggregation across regions of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple video cameras are installed to monitor a given area, then the coverage area is improved, but the difficulty of continuous tracking and analytics across camera views increases
Solution Approach 1:
The patent introduces an intermediary system comprising edge devices and a central server that mediates between multiple cameras. Edge devices extract features from camera feeds and generate vector codes, while the central server aggregates these codes to track objects across camera boundaries. This intermediary layer solves the discontinuity problem by providing a unified identification framework that transcends individual camera views.
Solution Approach 2:
The patent transforms object identification from pixel-based visual matching to parameter-based vector code comparison. By converting visual features into mathematical representations (vector codes with specific dimensions and characteristics), the system enables efficient cross-camera tracking through parameter comparison rather than complex image analysis, thereby reducing tracking difficulty across multiple views.
2Measurement precision
If full object images are transmitted and processed across the network, then object recognition accuracy is improved, but bandwidth usage and computational processing increase
Solution Approach 1:
The patent extracts only the essential features from full object images using edge devices. Instead of transmitting complete images, the system extracts salient visual characteristics and converts them into compact vector code representations. This extraction process retains sufficient information for accurate object recognition while dramatically reducing data transmission requirements and computational processing needs.
Solution Approach 2:
The patent creates simplified copies of objects in the form of vector codes rather than using original full-resolution images. These vector code copies contain the essential identifying characteristics needed for recognition and tracking, enabling accurate object identification with minimal data storage and transmission requirements, thereby reducing bandwidth usage and computational energy consumption.
Data Source
AI summary
Systems and methods are provided for object re-identification. In many scenarios, it would be useful to be able to monitor the movement, actions, etc. of an object, such as a person, moving into and between different camera views of a monitored space. A network of cameras and client edge devices may detect and identify a particular object, and when that object re-appears in another camera view, a comparison can be performed between the data collected regarding the object upon its initial appearance and upon its re-appearance to determine if they are the same object. In this way, data regarding an object can be captured, analyzed, or otherwise processed despite moving from one camera view to another.