Surveillance Vector Encoding for Low-Loss Edge-Cloud Transmission
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Solution Overview
Problem
Existing systems face challenges in reducing information loss and latency when transmitting data from edge devices to cloud devices, particularly due to the need for human-readable descriptions or full media transmission, which can lead to bandwidth inefficiencies and loss of data integrity.
Innovation Solution
Implementing vector encoding on edge devices to convert captured data into a shared vector space, allowing for more efficient communication with cloud devices using larger generative transformer models, thereby reducing information loss and bandwidth consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If human-readable descriptions or full media are transmitted from edge devices to cloud devices, then data integrity is maintained, but bandwidth consumption increases and information loss occurs
Solution Approach 1:
The patent transforms visual data from its original media format into a different parameter representation (vector embeddings) that preserves semantic information while reducing bandwidth requirements. This parameter transformation allows the system to maintain data integrity for AI processing purposes without transmitting the full original media, thus resolving the contradiction between information preservation and bandwidth consumption
Solution Approach 2:
The system creates a compressed representation (copy) of the original visual data in the form of vector embeddings. This copy contains the essential semantic information needed for AI processing but occupies significantly less bandwidth, allowing faithful reproduction of the data's meaning without transmitting the full original media file
2Loss of information
If vector encoding is implemented on edge devices, then bandwidth consumption is reduced and information loss is minimized, but device complexity increases
Solution Approach 1:
The system segments the processing architecture by placing the vector encoding model on the edge device and the generative transformer model on the cloud device. This segmentation allows the edge device to perform only the lightweight vector encoding operation while leveraging cloud resources for complex processing, thus managing device complexity while maintaining information fidelity
Solution Approach 2:
The vector embedding serves as an intermediary representation between the edge device capture and cloud device processing. This intermediary format simplifies the edge device's processing requirements while preserving the essential information needed for accurate cloud-based analysis, effectively mediating between limited edge resources and comprehensive cloud capabilities
3Power
If cloud devices process data remotely, then processing power is increased, but latency increases due to remote positioning
Solution Approach 1:
The system performs preliminary action by encoding data into vector representations at the edge device before transmission to the cloud. This preprocessing step reduces the complexity of data that needs to be transmitted and processed remotely, enabling faster cloud processing and reducing overall system latency while maintaining the benefits of cloud processing power
Data Source
AI summary
Various embodiments relate to systems including a surveillance unit. A system may include a surveillance unit comprising at least one camera for capturing data including one or more objects. The surveillance unit may also include a first model for generating at least one vector representation based on the one or more objects of the captured data. The system may also include a server communicatively coupled to the surveillance unit including a second model to receive the at least one vector representation and generate output data based on the at least one vector representation. The surveillance unit may further be configured to receive the output data and convey, via at least one output device, an output based on the output data. Associated methods are also disclosed.