A comparative study on SoC embedded low power GPUs for real-time edge-based automated traffic surveillance

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.

Automated summary

This article discusses the challenge of real-time processing for automated traffic surveillance and compares the suitability of SoCs embedded with low-power GPUs for edge devices. The study recommends optimization techniques and shows that real-time processing for HD videos can be achieved with implementations optimized for these SoCs.