Object Tracking of Aerial Imaging Device Image Using Variational Autoencoder and External Memory

被引：0
|
作者：
Park, Keunho [1 ]
Kim, Byoungjun [1 ]
Kim, Donghoon [1 ]
Kim, Seon-Hyeong [1 ]
Kim, Seo-jeong [1 ]
Jeong, Sunghwan [1 ]
机构：
[1] Korea Elect Technol Inst, Jeonju Si, South Korea
来源：
2022 THIRTEENTH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS (ICUFN) | 2022年
关键词：
deep learning; object tracking; variational autoencoder; external memory; Siamese network;
D O I：
10.1109/ICUFN55119.2022.9829672
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Object tracking is a fundamental problem in the field of computer vision. The object tracking methods proposed so far can be divided into a 'discriminative correlation filter' and a 'deep learning' based methods with a complex structure and a lot of computation. In this paper, we propose an algorithm for tracking objects with a simple structure while maintaining tracking performance using a convolutional variational auto-encoder, external memory, and a Siamese network. As a result of an experiment with an RT (real-time) data set to measure real-time, the result was a precision of 0.546 and a success rate of 0.527.
引用
收藏
页码：473 / 478
页数：6
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