Real Time Object Identification Using Deep Convolutional Neural Networks

被引：0

作者：

Sujana, Rajeswari S. ^{[1
]}

Abisheck, Sudar S. ^{[1
]}

Ahmed, Tauseef A. ^{[1
]}

Chandran, Sarath K. R. ^{[1
]}

机构：

[1] SSN Coll Engn, Madras, Tamil Nadu, India

来源：

2017 INTERNATIONAL CONFERENCE ON COMMUNICATION AND SIGNAL PROCESSING (ICCSP) | 2017年

关键词：

Real-time object identification; VGGNet; Residual Networks; Single Shot Multibox Detector;

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：

The project on Real Time Object Identification presents an approach to use the concept of deep learning with the convolutional neural networks in identifying the objects present when video is given as the input. The method uses the input video to give the output with the set of identified objects surrounded by boxes, even if the objects are of different sizes and shapes. Along with identification of the objects, the convolutional neural network works give the confidence score for each of the object. This methodology is called the Single Shot MultiBox Detector (SSD). In this project, we are replacing the VGG Net with Residual Networks in the architecture to increase the computational speed. [9]

引用

页码：1801 / 1805

页数：5