Vulnerable pedestrian detection and tracking using deep learning

被引：0

作者：

Song, Hyok ^{[1
]}

Choi, In Kyu ^{[1
]}

Ko, Min Soo ^{[1
]}

Bae, Jinwoo ^{[2
]}

Kwak, Sooyoung ^{[3
]}

Yoo, Jisang ^{[4
]}

机构：

[1] Korea Elect Technol Inst, Seongnam, South Korea

[2] Korea Intellectual Property Strategy Agcy, Seoul, South Korea

[3] Hanbat Univ, Daejeon, South Korea

[4] Kwangwoon Univ, Seoul, South Korea

来源：

2018 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC) | 2018年

关键词：

Pedestrian detection; Deep learning; Single shot multibox detector; mobilenet;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Accidents related with vulnerable pedestrians around crosswalks are continued so that proactive safety support system is required. Our research includes vulnerable pedestrian action character deduction, pedestrian action analysis module development and safety system development using big data analysis for the proactive safety support system. This paper shows pedestrian/Car detection, tracking and action recognition system using deep learning using video streams which come from CCTVs installed at SNU(Seoul National University). This algorithm includes SSD(Single shot multibox detector) structure and mobilenets for faster process and higher detection ratio.

引用

页码：178 / 179

页数：2