Paper Info
Title
Ju-Vnt: A Multi-Spectral Dataset Of Indoor Object Recognition Using Visible, Near-Infrared And Thermal Spectrum
Abstract
Detecting objects in natural scenes can be a very challenging task. In several real-life scenarios it is often found that visible spectrum is not ideal for typical computer vision tasks. Going beyond the range of visible light spectrum, such as the near infrared spectrum or the thermal spectrum allows us to capture many unique properties of objects that normally not captured with a normal camera. In this work we propose two multi-spectral dataset with three different spectrum, namely, the visible, near infrared and thermal spectrum. The first dataset is a single object dataset where we have common desk objects of 25 different categories comprising of various materials. The second dataset comprises of all possible combination using these 25 objects taking a pair at a time. The objects are captured from 8 different angles using the three different cameras. The images are registered and cropped and provided along with classification and localization ground truths. Additionally classification benchmarks have been provided using the ResNet, InceptionNet and DenseNet architectures on both the datasets. The dataset would be publicly available from .
Year
DOI
Venue
2021
10.1007/s11042-020-10302-z
MULTIMEDIA TOOLS AND APPLICATIONS
Keywords
DocType
Volume
Multispectral image processing, Thermal image, Near infrared image, Deep learning
Journal
80
Issue
ISSN
Citations 
12
1380-7501
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Swarnendu Ghosh1205.37
Nibaran Das239140.72
Priyam Sarkar300.34
Mita Nasipuri4725107.01