Accurate Object Detection in Smart Transportation Using Multiple Cameras

被引：0

作者：

Qiao, Zhinan ^{[1
]}

Sansom, Andrew ^{[1
]}

McGuire, Mara ^{[2
]}

Kalaani, Andrew ^{[3
]}

Ma, Xu ^{[1
]}

Yang, Qing ^{[1
]}

Fu, Song ^{[1
]}

机构：

[1] Univ North Texas, Denton, TX 76203 USA

[2] Texas A&M Univ Corpus Christi, Corpus Christi, TX 78412 USA

[3] Georgia Southern Univ, Statesboro, GA 30458 USA

来源：

2020 INTERNATIONAL CONFERENCE ON CONNECTED AND AUTONOMOUS DRIVING (METROCAD 2020) | 2020年

关键词：

multi-view; alignment; fusion network; object detection; IMAGE FUSION; ALIGNMENT;

D O I：

10.1109/MetroCAD48866.2020.00011

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recently, more and more attention has been paid to the connected object detection for better performance. One of the most interesting fields is learning from multiple resources in a connected fashion. In this paper, we present a connected object detection method using multiple cameras for the smart transportation system. The proposed architecture consists of three parts: an alignment framework, a deep multi-view fusion network and an object detection network. Experiments are conducted to illustrate the performance of our proposed architecture.

引用

页码：27 / 33

页数：7

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