Customer Recognition and Counting by Cloud Computing

Dersingh, Anand; Charanyananda, Sunchanan; Chaiyaprom, Apusara; Domsrifah, Nuchchada; Liwsakphaiboon, Santi

doi:10.1109/itc-cscc.2019.8793318

Cited by 1 publication

(2 citation statements)

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“…There has been a lot of debate on the strategies for the deployment of DL applications. One common way is to offload and perform tasks that are computationally expensive on the cloud [ 14 , 15 ]. The second approach is to perform computations closer to the edge and only send the metadata to the cloud [ 16 , 17 ].…”

Section: Proposed Edge–cloud Systemmentioning

confidence: 99%

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Optimizing Face Recognition Inference with a Collaborative Edge–Cloud Network

Oroceo

Kim

Caliwag

et al. 2022

Sensors

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The rapid development of deep-learning-based edge artificial intelligence applications and their data-driven nature has led to several research issues. One key issue is the collaboration of the edge and cloud to optimize such applications by increasing inference speed and reducing latency. Some researchers have focused on simulations that verify that a collaborative edge–cloud network would be optimal, but the real-world implementation is not considered. Most researchers focus on the accuracy of the detection and recognition algorithm but not the inference speed in actual deployment. Others have implemented such networks with minimal pressure on the cloud node, thus defeating the purpose of an edge–cloud collaboration. In this study, we propose a method to increase inference speed and reduce latency by implementing a real-time face recognition system in which all face detection tasks are handled on the edge device and by forwarding cropped face images that are significantly smaller than the whole video frame, while face recognition tasks are processed at the cloud. In this system, both devices communicate using the TCP/IP protocol of wireless communication. Our experiment is executed using a Jetson Nano GPU board and a PC as the cloud. This framework is studied in terms of the frame-per-second (FPS) rate. We further compare our framework using two scenarios in which face detection and recognition tasks are deployed on the (1) edge and (2) cloud. The experimental results show that combining the edge and cloud is an effective way to accelerate the inferencing process because the maximum FPS achieved by the edge–cloud deployment was 1.91× more than the cloud deployment and 8.5× more than the edge deployment.

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Section: Proposed Edge–cloud Systemmentioning

confidence: 99%

“…DL-based face recognition has two parts: training and inference. It is more suitable to perform the training process in the cloud because the process requires high throughput computing and a huge amount of data [ 14 , 15 ]. The cloud can provide sufficient resources for processing, storage, and energy.…”

Section: Introductionmentioning

confidence: 99%