1,655 research outputs found
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection
Multispectral pedestrian detection has received extensive attention in recent
years as a promising solution to facilitate robust human target detection for
around-the-clock applications (e.g. security surveillance and autonomous
driving). In this paper, we demonstrate illumination information encoded in
multispectral images can be utilized to significantly boost performance of
pedestrian detection. A novel illumination-aware weighting mechanism is present
to accurately depict illumination condition of a scene. Such illumination
information is incorporated into two-stream deep convolutional neural networks
to learn multispectral human-related features under different illumination
conditions (daytime and nighttime). Moreover, we utilized illumination
information together with multispectral data to generate more accurate semantic
segmentation which are used to boost pedestrian detection accuracy. Putting all
of the pieces together, we present a powerful framework for multispectral
pedestrian detection based on multi-task learning of illumination-aware
pedestrian detection and semantic segmentation. Our proposed method is trained
end-to-end using a well-designed multi-task loss function and outperforms
state-of-the-art approaches on KAIST multispectral pedestrian dataset
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
Effective fusion of complementary information captured by multi-modal sensors
(visible and infrared cameras) enables robust pedestrian detection under
various surveillance situations (e.g. daytime and nighttime). In this paper, we
present a novel box-level segmentation supervised learning framework for
accurate and real-time multispectral pedestrian detection by incorporating
features extracted in visible and infrared channels. Specifically, our method
takes pairs of aligned visible and infrared images with easily obtained
bounding box annotations as input and estimates accurate prediction maps to
highlight the existence of pedestrians. It offers two major advantages over the
existing anchor box based multispectral detection methods. Firstly, it
overcomes the hyperparameter setting problem occurred during the training phase
of anchor box based detectors and can obtain more accurate detection results,
especially for small and occluded pedestrian instances. Secondly, it is capable
of generating accurate detection results using small-size input images, leading
to improvement of computational efficiency for real-time autonomous driving
applications. Experimental results on KAIST multispectral dataset show that our
proposed method outperforms state-of-the-art approaches in terms of both
accuracy and speed
Deep visible and thermal image fusion for enhanced pedestrian visibility
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
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