实时行人检测和跟踪定制硬件

2016 14th ACM/IEEE Symposium on Embedded Systems For Real-time Multimedia (ESTIMedia) Pub Date : 2016-10-01 DOI:10.1145/2993452.2995268

Junbin Wang, Ke Yan, Kaiyuan Guo, Jincheng Yu, Lingzhi Sui, Song Yao, Song Han, Yu Wang

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引用次数: 6

摘要

实时行人检测和跟踪对于许多应用至关重要，例如无人机与人之间的交互。然而，卷积神经网络(CNN)的高复杂性使其依赖于强大的服务器，因此很难用于无人机等移动平台。本文提出了一种基于cnn的实时行人检测与跟踪系统，该系统仅用3W即可实现14.7 fps的检测和200 fps的跟踪。

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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Real-time pedestrian detection and tracking on customized hardware

Real-time pedestrian detection and tracking are vital to many applications, such as the interaction between drones and human. However, the high complexity of Convolutional Neural Network (CNN) makes them rely on powerful servers, thus is hard for mobile platforms like drones. In this paper, we propose a CNN-based real-time pedestrian detection and tracking system, which can achieve 14.7 fps detection and 200 fps tracking with only 3W.

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2016 14th ACM/IEEE Symposium on Embedded Systems For Real-time Multimedia (ESTIMedia)

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