Abstract
Video face detection and recognition in public places at the edge is required in several applications, such as security reinforcement and contactless access to authorized venues. This paper aims to maximize the simultaneous usage of hardware engines available in edge GPUs nowadays by leveraging the concurrency and pipelining of tasks required for face detection and recognition. This also includes the video decoding task, which is required in most face monitoring applications as the video streams are usually carried via Gbps Ethernet network. This constitutes an improvement over previous works where the tasks are usually allocated to a single engine due to the lack of a unified and automated framework that simultaneously explores all hardware engines. In addition, previously, the input faces were usually embedded in still images or within raw video streams that overlook the burst delay caused by the decoding stage. The results on real-life video streams suggest that simultaneously using all the hardware engines available in the recent NVIDIA edge Orin GPU, higher throughput, and a slight saving of power consumption of around 300 mW, accounting for around 5%, have been achieved while satisfying the real-time performance constraint. The performance gets even higher by considering several video streams simultaneously. Further performance improvement could have been obtained if the number of shuffle layers that were created by the tensor RT framework for the face recognition task was lower. Thus, the paper suggests some hardware improvements to the existing edge GPU processors to enhance their performance even higher.
Abstract (translated)
在公共场所进行视频面部检测和识别的需求在多个应用中存在，例如加强安全性和实现授权场所的非接触式访问。本文旨在通过利用当前边缘GPU硬件引擎的并发性和流水线作业来最大化这些引擎的同时使用，从而优化面部检测和识别任务。这包括大多数面部监控应用程序所需的视频解码任务，因为视频流通常通过Gbps以太网网络传输。 与以往的工作相比，这项研究有所改进：过去由于缺乏统一且自动化的框架，任务通常被分配给单一硬件引擎进行处理。此外，在之前的实践中，输入的面部通常是嵌入在静态图像或原始视频流中，并未考虑到解码阶段可能造成的突发延迟。实际视频流中的测试结果表明，在最近的NVIDIA边缘Orin GPU上同时使用所有可用硬件引擎能够实现更高的吞吐量和大约300毫瓦（约占5%）的功率节省，这在满足实时性能约束的同时得以体现。此外，考虑到多个视频流时，性能将进一步提高。 进一步的性能改进本可通过减少由TensorRT框架为面部识别任务创建的shuffle层数量来实现。因此，论文建议对现有的边缘GPU处理器进行一些硬件改进以进一步提升其性能。
URL
https://arxiv.org/abs/2505.04502
