Distributed Video Analytics DNN Partitioning and Compression
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Solution Overview
Problem
Existing methods for video analytics in distributed systems face challenges in optimizing computational complexity and compression efficiency, leading to suboptimal performance in tasks such as object detection and image classification due to high computational load and poor compression techniques that prioritize perceptual quality over semantic representation.
Innovation Solution
The implementation of dynamic DNN partitioning with variable bit rate compression and motion estimation/optical flow-based processing, which involves splitting the DNN at a specific layer to extract deep feature representations, compressing them, and transmitting only the necessary information, along with the introduction of a bottleneck module to reduce data dimensions and using deep feature warping to enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used to provide accurate solutions for video analytics tasks, then accuracy is improved, but computational intensity and memory demands increase
Solution Approach 1:
The system segments the video processing task by extracting and transmitting only key frames (e.g., every Nth frame) rather than all frames, reducing the total computational load while maintaining analytical accuracy through selective processing of representative frames
Solution Approach 2:
The system extracts only the essential deep feature representations from the neural network processing and transmits these compressed features to the cloud for further analysis, rather than transmitting the complete video data, thereby reducing computational intensity
2Measurement precision
If deep neural networks are used to provide accurate solutions for video analytics tasks, then accuracy is improved, but memory demands increase
Solution Approach 1:
The system extracts and transmits only the compressed deep feature representations rather than the complete video data, significantly reducing the quantity of data stored and processed in memory while preserving the essential information needed for accurate analytics
Solution Approach 2:
The system performs preliminary compression of video data into key frames and deep feature representations before transmission to the cloud, reducing the memory burden on both edge devices and cloud servers
3Manufacturing precision
If compression techniques prioritize perceptual quality, then visual quality is improved, but compression efficiency for semantic representation deteriorates
Solution Approach 1:
The system applies different compression strategies to different aspects of the data: aggressive compression is applied to the visual data for transmission efficiency, while the neural network preserves semantic information through deep feature extraction, achieving both compression efficiency and semantic fidelity
Solution Approach 2:
The deep feature representations serve as an intermediary between the compressed visual data and the semantic analysis, preserving the essential semantic information while allowing aggressive compression of the visual data itself
Data Source
AI summary
Methods and apparatus are disclosed herein for computation and compression efficiency in distributed video analytics. Example apparatus disclosed herein are to identify a key frame and a non-key frame in a video frame sequence input to a neural network at a client server, determine motion information between the key frame and the non-key frame based on optical flow, and determine a frame feature representation based on the motion information reconstructed at an edge server, the motion information including feature warping residual errors.