Edge-GPU Based Face Tracking for Face Detection and Recognition Acceleration

TL;DR

This paper presents a hardware-software co-design approach leveraging NVIDIA Jetson AGX Orin's full hardware capabilities and face tracking to significantly improve real-time face detection and recognition efficiency while reducing power consumption.

Contribution

It introduces a novel method that utilizes all GPU engines and face tracking on edge devices, enhancing throughput and energy efficiency over prior approaches.

Findings

Achieved 290 FPS processing speed on 1080p frames with multiple faces.

Reduced power consumption by approximately 800 mW.

Demonstrated effective integration of face tracking to optimize recognition tasks.

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.