A High-Performance Pedestrian Detector and Its Implementation on Embedded Systems for Hypermarket Environment

2019 International SoC Design Conference (ISOCC) Pub Date : 2019-10-01 DOI:10.1109/ISOCC47750.2019.9027682

Kuan-Hung Chen, Jesse Der-Chian Deng, Y. Hwang

引用次数: 6

Abstract

Enabling autonomous driving in hypermarket environments is a new challenge. The whole scenario is very different from traditional outdoor autonomous driving. To navigate in hypermarket environments, the vehicles need to know where all the surrounded pedestrians are. In addition, the detection model must be small enough to be executed in real-time speed on embedded systems. Therefore, we present a high-performance convolutional neural network for detecting moving indoor pedestrians as well as its implementation on embedded systems in this paper. The proposed CNN model can achieve the same high accuracy as YOLO v3 at the cost of only 27% of the original model size. When implemented on an embedded system, i.e., Jetson Xavier, this work achieves 30 fps @ 360p video format.

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大型超市环境下的高性能行人检测器及其嵌入式系统实现

在大卖场环境中实现自动驾驶是一个新的挑战。整个场景与传统的户外自动驾驶非常不同。为了在大型超市环境中导航，车辆需要知道周围行人的位置。此外，检测模型必须足够小，以便在嵌入式系统上以实时速度执行。因此，本文提出了一种用于室内移动行人检测的高性能卷积神经网络及其在嵌入式系统上的实现。本文提出的CNN模型可以达到与YOLO v3相同的高精度，而成本仅为原始模型尺寸的27%。当在嵌入式系统上实现时，即Jetson Xavier，这项工作实现了30 fps @ 360p的视频格式。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 International SoC Design Conference (ISOCC)

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