Abstract
Cost-effective machine vision systems dedicated to real-time and accurate face detection and recognition in public places are crucial for many modern applications. However, despite their high performance, which could be reached using specialized edge or cloud AI hardware accelerators, there is still room for improvement in throughput and power consumption. This paper aims to suggest a combined hardware-software approach that optimizes face detection and recognition systems on one of the latest edge GPUs, namely NVIDIA Jetson AGX Orin. First, it leverages the simultaneous usage of all its hardware engines to improve processing time. This offers an improvement over previous works where these tasks were mainly allocated automatically and exclusively to the CPU or, to a higher extent, to the GPU core. Additionally, the paper suggests integrating a face tracker module to avoid redundantly running the face recognition algorithm for every frame but only when a new face appears in the scene. The results of extended experiments suggest that simultaneous usage of all the hardware engines that are available in the Orin GPU and tracker integration into the pipeline yield an impressive throughput of 290 FPS (frames per second) on 1920 x 1080 input size frames containing in average of 6 faces/frame. Additionally, a substantial saving of power consumption of around 800 mW was achieved when compared to running the task on the CPU/GPU engines only and without integrating a tracker into the Orin GPU\'92s pipeline. This hardware-codesign approach can pave the way to design high-performance machine vision systems at the edge, critically needed in video monitoring in public places where several nearby cameras are usually deployed for a same scene.
Abstract (translated)
高效的机器视觉系统在公共场所进行实时且精确的人脸检测和识别对于许多现代应用至关重要。尽管通过使用专门的边缘或云端AI硬件加速器可以实现高性能，但在吞吐量和功耗方面仍存在改进空间。本文提出了一种结合软硬件的方法，旨在优化基于最新边缘GPU（NVIDIA Jetson AGX Orin）的人脸检测和识别系统。 首先，该方法利用了所有可用的硬件引擎的同时使用来提高处理时间。这比以往只将任务主要分配给CPU或更大程度上专用于GPU核心的做法有所改进。此外，还建议在系统中集成一个面部追踪模块，以避免对每一帧都运行人脸识别算法，而是在新的面孔出现在场景时才进行。 通过广泛的实验结果表明，在Orin GPU上同时使用所有可用的硬件引擎，并将跟踪器整合到管道中，可以实现每秒290帧（FPS）的惊人吞吐量，输入为1920 x 1080尺寸的画面且平均每帧包含6张面孔。此外，与仅在CPU/GPU引擎上运行任务而不集成追踪器相比，在Orin GPU的管道内整合追踪器实现了大约800毫瓦（mW）的功耗节省。 这种软硬件协同设计的方法为边缘高绩效机器视觉系统的开发铺平了道路，这对于公共场所中的视频监控尤为关键，通常需要部署多个近距离摄像头来覆盖同一场景。
URL
https://arxiv.org/abs/2505.04524
