A comparative study on SoC embedded low power GPUs for real‐time edge‐based automated traffic surveillance. (3rd December 2021)

• Record Type: Journal Article
• Authors: Jaiswal, Deepak; Kumar, Praveen
• Is Part Of: Concurrency and computation. Volume 34:Number 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-03
• Subjects: automated traffic surveillance -- NVIDIA Jetson Nano -- parallel edge computing -- Qualcomm Adreno GPU -- smart camera -- wrong‐way vehicle detection; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6736
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Achieving real‐time processing for automated traffic surveillance is a major challenge due to the huge amount of data generated by a large number of surveillance cameras. For this, centralized computing has been a standard architecture for many years. But due to the increasing need for real‐time processing and limitations of centralized computing architecture (such as network congestion due to limited bandwidth and the need for costly high‐end servers), the paradigm is shifting toward edge processing. This article compares the suitability of two popular but different types of SoCs (System on Chip) (from NVIDIA and Qualcomm) embedded with low‐power GPUs as processing units for edge devices. These GPUs can be programmed as general‐purpose GPUs (GPGPU) to speed up compute‐intensive tasks, so as to achieve real‐time processing at the edge of the network. The article also discusses the architectural features and differences between these GPUs and recommends optimization techniques to leverage them. For quantitative comparison, we implement a wrong‐way vehicle detection algorithm (using background‐subtraction‐based moving object detection and Kalman‐filter‐based trajectory tracking), and optimized it for these SoCs. The experimental results show that real‐time processing can be achieved for HD videos with implementations optimized for these SoCs.