An In-Vehicle System for Pedestrian Detection

被引：1
|
作者：
Liu, Gang [1 ]
Sun, Yufen [2 ]
机构：
[1] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430070, Peoples R China
[2] Wuhan Univ Technol, Coll Comp Sci & Technol, Wuhan, Peoples R China
来源：
2012 11TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS TO BUSINESS, ENGINEERING & SCIENCE (DCABES) | 2012年
基金：
中国国家自然科学基金;
关键词：
pedestrian detection; in-vehicle; GPU; parallel processing; CENTRIST descriptor;
D O I：
10.1109/DCABES.2012.80
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Pedestrian detection is a significant task in driver assistance systems. This paper presents an in-vehicle system for pedestrian detection. To speed up computation, we parallel implemented the state of the art detection algorithm on a NVIDIA GPU. The experiments demonstrate that our implementation can achieve accurate and real time detection.
引用
收藏
页码：328 / 331
页数：4
相关论文
共 50 条
  • [31] Intrusion detection system using SOEKS and deep learning for in-vehicle security
    Lulu Gao
    Fei Li
    Xiang Xu
    Yong Liu
    Cluster Computing, 2019, 22 : 14721 - 14729
  • [32] An Intrusion Detection System for In-vehicle CAN Network Based on Sample Entropy
    Cui Y.
    Zhang Y.
    Wei H.
    Qiche Gongcheng/Automotive Engineering, 2023, 45 (07): : 1184 - 1191
  • [33] Supervised contrastive ResNet and transfer learning for the in-vehicle intrusion detection system
    Hoang, Thien-Nu
    Kim, Daehee
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 238
  • [34] Robust Road Condition Detection System Using In-Vehicle Standard Sensors
    Castillo Aguilar, Juan Jess
    Cabrera Carrillo, Juan Antonio
    Guerra Fernandez, Antonio Jesus
    Carabias Acosta, Enrique
    SENSORS, 2015, 15 (12) : 32056 - 32078
  • [35] Intrusion detection system using SOEKS and deep learning for in-vehicle security
    Gao, Lulu
    Li, Fei
    Xu, Xiang
    Liu, Yong
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2019, 22 (Suppl 6): : 14721 - 14729
  • [36] An intrusion detection method for the in-vehicle network
    Cheng, Anyu
    Peng, Yibo
    Yan, Hao
    Shen, Xiaona
    PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 4893 - 4899
  • [37] A Survey of Intrusion Detection for In-Vehicle Networks
    Wu, Wufei
    Li, Renfa
    Xie, Guoqi
    An, Jiyao
    Bai, Yang
    Zhou, Jia
    Li, Keqin
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2020, 21 (03) : 919 - 933
  • [38] A Lightweight Intrusion Detection System on In-Vehicle Network Using Polynomial Features
    Sukhbaatar, Baatarsuren
    Islam, Md Rezanur
    Yuspov, Kamronbek
    Oh, Insu
    Yim, Kangbin
    INNOVATIVE MOBILE AND INTERNET SERVICES IN UBIQUITOUS COMPUTING, IMIS-2023, 2023, 177 : 96 - 102
  • [39] Towards In-vehicle Driver Fainting Detection
    Gebert, Moritz
    Kobiela, Fanny
    Leibinger, Julia
    Mueller, Hermann
    PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON AUTOMOTIVE USER INTERFACES AND INTERACTIVE VEHICULAR APPLICATIONS, AUTOMOTIVEUI 2023, 2023, : 244 - 255
  • [40] Look at Me: Augmented Reality Pedestrian Warning System Using an In-vehicle Volumetric Head Up Display
    Kim, Hyungil
    Anon, Alexandre Miranda
    Misu, Teruhisa
    Li, Nanxiang
    Tawari, Ashish
    Fujimura, Kikuo
    PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES (IUI'16), 2016, : 294 - 298
12345