Deep Visible and Thermal Image Fusion with Cross-Modality Feature Selection for Pedestrian Detection

2021 ◽  
pp. 117-127
Author(s):  
Mingyue Li ◽  
Zhenzhou Shao ◽  
Zhiping Shi ◽  
Yong Guan
Keyword(s):  
Feature Selection ◽  
Image Fusion ◽  
Pedestrian Detection ◽  
Thermal Image ◽  
Selection For

scholarly journals Deep Visible and Thermal Image Fusion for Enhanced Pedestrian Visibility

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3727 ◽  
Author(s):  
Ivana Shopovska ◽  
Ljubomir Jovanov ◽  
Wilfried Philips
Keyword(s):  
Image Fusion ◽  
Pedestrian Detection ◽  
Thermal Image ◽  
Natural Image ◽  
Objective Functions ◽  
Automotive Systems ◽  
Human Driver ◽  
Thermal Cameras ◽  
Image Appearance ◽  
Rgb Images

Reliable vision in challenging illumination conditions is one of the crucial requirements of future autonomous automotive systems. In the last decade, thermal cameras have become more easily accessible to a larger number of researchers. This has resulted in numerous studies which confirmed the benefits of the thermal cameras in limited visibility conditions. In this paper, we propose a learning-based method for visible and thermal image fusion that focuses on generating fused images with high visual similarity to regular truecolor (red-green-blue or RGB) images, while introducing new informative details in pedestrian regions. The goal is to create natural, intuitive images that would be more informative than a regular RGB camera to a human driver in challenging visibility conditions. The main novelty of this paper is the idea to rely on two types of objective functions for optimization: a similarity metric between the RGB input and the fused output to achieve natural image appearance; and an auxiliary pedestrian detection error to help defining relevant features of the human appearance and blending them into the output. We train a convolutional neural network using image samples from variable conditions (day and night) so that the network learns the appearance of humans in the different modalities and creates more robust results applicable in realistic situations. Our experiments show that the visibility of pedestrians is noticeably improved especially in dark regions and at night. Compared to existing methods we can better learn context and define fusion rules that focus on the pedestrian appearance, while that is not guaranteed with methods that focus on low-level image quality metrics.

Grey wolf optimiser-based feature selection for feature-level multi-focus image fusion

2020 ◽  
Vol 16 (3) ◽  
pp. 279
Author(s):  
K. Sujatha ◽  
D. Shalini Punithavathani ◽  
J. Janet ◽  
S. Venkatalakshmi
Keyword(s):  
Feature Selection ◽  
Image Fusion ◽  
Grey Wolf ◽  
Focus Image ◽  
Selection For

Grey-Wolf Optimizer Based Feature Selection for Feature-Level Multi-Focus Image Fusion

2018 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Sujatha K ◽  
D. Shalini Punithavathani ◽  
J. Janet ◽  
S. Venkatalakshmi
Keyword(s):  
Feature Selection ◽  
Image Fusion ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Focus Image ◽  
Selection For
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