Improved deeplab v3+ with metadata extraction for small object detection in intelligent visual surveillance systems

被引：0

作者：

Oh H. ^{[1
]}

Lee M. ^{[1
]}

Kim H. ^{[1
]}

Paik J. ^{[1
]}

机构：

[1] Department of Image Engineering, Processing and Intelligent Systems Laboratory, Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University /, Seoul

来源：

IEIE Transactions on Smart Processing and Computing | 2021年 / 10卷 / 03期

关键词：

Metadata; Object segmentation; Surveillance system;

D O I：

10.5573/IEIESPC.2021.10.3.209

中图分类号：

学科分类号：

摘要：

A surveillance system deploys multiple cameras to monitor a wide area in real time to detect abnormal situations such as a crime scene, traffic accident, and natural disaster. An Increased number of cameras results in the same number of monitors, which makes human decisions or automatic decisions difficult. To solve the problem, a smart surveillance scheme has recently been proposed. The smart surveillance system automatically detects an object and provides an alarm to a surveillant. In this paper, we present a metadata extraction method for object-based video summary. The proposed method adopts deep learning-based object detection and background elimination to correctly estimate an object region. Finally, metadata extraction is performed on the estimated object information. The proposed metadata consists of the representative color, size, aspect ratio, and patch of an object. The proposed method can extract reliable metadata without motion features in both static and dynamic cameras. The proposed method can be applied to various object detection areas using complex metadata. Copyrights © 2021 The Institute of Electronics and Information Engineers

引用

页码：209 / 218

页数：9