Edge Video Analysis via Encoder Network Compression
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Solution Overview
Problem
Current video analysis systems face challenges in efficiently processing and transmitting large volumes of video data from edge locations to central servers due to bandwidth and computational resource constraints, making it expensive and infeasible to aggregate and analyze data effectively.
Innovation Solution
Implementing a method where edge devices compress and classify video frames using encoder and classification networks, reducing data size and bandwidth requirements by transmitting only action classification information to the central server, allowing for efficient video analysis at the edge before data is sent to the cloud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video data is transmitted from edge locations to central server for analysis, then comprehensive video analysis can be performed, but bandwidth requirements and transmission costs increase significantly
Solution Approach 1:
The patent extracts only the essential action classification information from video frames at the edge device, rather than transmitting the entire video data. The encoder network processes video frames locally and extracts compressed frame features that capture only the necessary action information, which is then transmitted to the central server for analysis.
Solution Approach 2:
The patent segments the video analysis process into two parts: edge-based compression and feature extraction, and central-based action classification. The encoder network at the edge device performs initial processing and segmentation of video data, separating essential action features from redundant information before transmission.
2Measurement precision
If complete video frames are processed and transmitted for action classification, then accurate action recognition can be achieved, but computational load and processing time increase
Solution Approach 1:
The patent applies preliminary action by performing compression and feature extraction before action classification. The encoder network pre-processes video frames at the edge device, extracting compressed frame features that contain essential action information, which then feeds into the action classification network at the central server.
Solution Approach 2:
The encoder network extracts only the essential features necessary for action classification from complete video frames, removing redundant information. This extraction process occurs at the edge device, providing simplified compressed frame features to the central server for efficient classification.
3Quantity of substance
If video data is compressed using traditional compression methods, then bandwidth requirements are reduced, but action classification accuracy deteriorates
Solution Approach 1:
The patent replaces traditional mechanical compression methods with a neural network-based encoder. The encoder network learns optimal compression representations through training, substituting conventional compression algorithms with an intelligent system that preserves action-relevant features while reducing data volume.
Solution Approach 2:
The patent changes the parameter representation from raw video pixels to compressed frame features extracted by the encoder network. This parameter transformation optimizes the data representation for action classification, maintaining essential action information while reducing transmission requirements.
Data Source
AI summary
Systems and methods for image processing are described. The systems and methods include receiving a plurality of frames of a video at an edge device, wherein the video depicts an action that spans the plurality of frames, compressing, using an encoder network, each of the plurality of frames to obtain compressed frame features, wherein the compressed frame features include fewer data bits than the plurality of frames of the video, classifying, using a classification network, the compressed frame features at the edge device to obtain action classification information corresponding to the action in the video, and transmitting the action classification information from the edge device to a central server.