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A Binarized Segmented ResNet Based on Edge Computing for Re-Identification.

Yanming Chen¹, Tianbo Yang¹, Chao Li²

¹School of Computer Science, Anhui University, Hefei 230601, China.

Sensors (Basel, Switzerland)

|December 8, 2020

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This study introduces a novel edge computing approach for person Re-Identification (ReID), significantly reducing communication costs by four to eight times. The method achieves this by segmenting a ResNet model across edge devices and the cloud without compromising recognition accuracy.

Area of Science:

Computer Vision
Artificial Intelligence
Networked Systems

Background:

Increasingly connected devices in urban environments necessitate efficient public safety solutions.
Current person Re-Identification (ReID) methods rely on cloud processing, leading to substantial communication overhead.
Edge computing offers a promising alternative to mitigate these communication burdens.

Purpose of the Study:

To develop an efficient person Re-Identification (ReID) system leveraging edge computing.
To reduce the communication costs associated with processing pedestrian images for ReID.
To maintain high recognition accuracy while minimizing data transfer.

Main Methods:

A binarized segmented ResNet model was proposed, dividing the network into three parts: end devices, edge, and cloud.

Keywords:

binary neural network cloud computing edge computing end devices person Re-Identification

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Joint training of the segmented sub-networks was performed.

The segmented sub-networks were deployed to their respective locations (end devices, edge, cloud) for inference.

Main Results:

The proposed method significantly reduced communication costs, by four to eight times compared to traditional ReID approaches.
Recognition accuracy was largely maintained, with minimal reduction compared to existing methods.
Demonstrated the feasibility and effectiveness of edge computing for distributed ReID tasks.

Conclusions:

Edge computing, combined with a segmented neural network architecture, provides an effective solution for reducing communication costs in person Re-Identification.
The proposed binarized segmented ResNet offers a practical approach for deploying ReID systems in resource-constrained environments.
This work paves the way for more scalable and efficient intelligent surveillance systems in smart cities.