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RGB-T Image Saliency Detection via Collaborative Graph Learning [article]

Zhengzheng Tu, Tian Xia, Chenglong Li, Xiaoxiao Wang, Yan Ma, Jin Tang
2019 arXiv   pre-print
Fusing complementary RGB and thermal infrared data has been proven to be effective for image saliency detection. In this paper, we propose an effective approach for RGB-T image saliency detection.  ...  Moreover, we contribute a more challenging dataset for the purpose of RGB-T image saliency detection, which contains 1000 spatially aligned RGB-T image pairs and their ground truth annotations.  ...  For the problem of grayscale-thermal foreground detection, Yang et al.  ... 
arXiv:1905.06741v1 fatcat:eyt4zazp3bgnzkfk7xllkmab2y

HSMD: An object motion detection algorithm using a Hybrid Spiking Neural Network Architecture

Pedro Machado, Andreas Oikonomou, Joao Filipe Ferreira, T.M. McGinnity
2021 IEEE Access  
The algorithm was compared against existing background subtraction (BS) approaches, available on the OpenCV library, specifically on the 2012 change detection (CDnet2012) and the 2014 change detection  ...  The results show that the HSMD was ranked overall first among the competing approaches and has performed better than all the other algorithms on four of the categories across all the eight test metrics  ...  The synaptic weights obtained experimentally are 1370 for all the synapses. The Layer 2 to Layer 4 weights were tuned to forward all the spike events generated in Layer 2.  ... 
doi:10.1109/access.2021.3111005 fatcat:e2jj5rcgvfghxmxdj2dqoebsou

Overview and Benchmarking of Motion Detection Methods [chapter]

Pierre-Marc Jodoin, Sébastien Piérard, Yi Wang, Marc Van Droogenbroeck
2014 Background Modeling and Foreground Detection for Video Surveillance  
Grayscale motion detection methods are normally used on mono-channel cameras like depth cameras, thermal cameras, or older grayscale surveillance cameras.  ...  Of course, the improvement rate is more significant for a low ranked method than for a precise one.  ...  Post-processing : Every post-processing method that we have tested improved the results of our motion detection methods, especially for the simple low-ranked method.  ... 
doi:10.1201/b17223-30 fatcat:wi6nuhjdqvbzlh7ehdpouukshi

Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models

Rui Wang, Filiz Bunyak, Guna Seetharaman, Kannappan Palaniappan
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops  
tensor formulation, a novel foreground and background modeling scheme, and a multi-cue appearance comparison.  ...  Extensive testing performed on the CVPR 2014 Change Detection benchmark dataset shows that FTSG outperforms state-ofthe-art methods.  ...  A new Gaussian is initialized with a high variance and low weight, and its mean is set to the current pixel value.  ... 
doi:10.1109/cvprw.2014.68 dblp:conf/cvpr/WangBSP14 fatcat:56cfprdednfrlg4lwmki7xzkfu

A Comprehensive Survey of Video Datasets for Background Subtraction

Rudrika Kalsotra, Sakshi Arora
2019 IEEE Access  
subtraction is an effective method of choice when it comes to detection of moving objects in videos and has been recognized as a breakthrough for the wide range of applications of intelligent video analytics  ...  Current trends of deep learning in background subtraction along with top-ranked background subtraction methods are also discussed in this paper.  ...  The LRSLibrary [206] is a collection of more than 100 low-rank and sparse decomposition algorithms for motion segmentation in videos.  ... 
doi:10.1109/access.2019.2914961 fatcat:thr65j4uivehpgxtkwbsqi3yc4

Introduction to the Special Section on Augmented Video

Peter Eisert, Yebin Liu, Kyuong Mu Lee, Didier Stricker, Graham Thomas
2017 IEEE transactions on circuits and systems for video technology (Print)  
The next paper, entitled "WELD: Weighted Low-Rank Decomposition for Robust Grayscale-Thermal Foreground Detection," by Li et al. also targets the image registration problem, but between grayscale and thermal  ...  His primary research interests include scene understanding, object recognition, low-level vision, visual tracking, and visual navigation. Dr.  ...  He currently leads the Immersive and Interactive Content Section, Research and Development Department, BBC, developing technology for new forms of content, with a focus on computer vision, graphics, and  ... 
doi:10.1109/tcsvt.2017.2681378 fatcat:kybflumsunh25kcnkzbipskjbu

Comparative study of motion detection methods for video surveillance systems

Kamal Sehairi, Fatima Chouireb, Jean Meunier
2017 Journal of Electronic Imaging (JEI)  
The objective of this study is to compare several change detection methods for a mono static camera and identify the best method for different complex environments and backgrounds in indoor and outdoor  ...  However, this study enables the user to identify the most suitable method for his or her needs.  ...  To rank all the methods, for each category c, we computed the rank of each method for metric M.  ... 
doi:10.1117/1.jei.26.2.023025 fatcat:awrtfpswtrbitn7yd6lsazwn3q

Deep Visible and Thermal Image Fusion for Enhanced Pedestrian Visibility

Ivana Shopovska, Ljubomir Jovanov, Wilfried Philips
2019 Sensors  
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  ...  ; and an auxiliary pedestrian detection error to help defining relevant features of the human appearance and blending them into the output.  ...  and low contrast in the shadow in RGB; low contrast in thermal range.  ... 
doi:10.3390/s19173727 pmid:31466378 pmcid:PMC6749306 fatcat:ykjkwe7xhbh5pf3f54ez34soi4

Spatiotemporal Tree Filtering for Enhancing Image Change Detection

Dawei Li, Siyuan Yan, Mingbo Zhao, Tommy W. S. Chow
2020 IEEE Transactions on Image Processing  
We, instead, solve the problem from another perspective by enhancing the raw detection results after change detection.  ...  In this paper, we propose Fast Spatiotemporal Tree Filter (FSTF), a purely unsupervised detection method, to enhance coarse binary detection masks obtained by different kinds of change detection methods  ...  [16] show that RPCA can be applied to moving object detection by modeling the background with a low-rank subspace and representing the foreground objects with a noise component. Zhou et al.  ... 
doi:10.1109/tip.2020.3017339 pmid:32833635 fatcat:pjnibdle65gbjoep6yokkjaeoq

Deep neural network concepts for background subtraction: A systematic review and comparative evaluation

Thierry Bouwmans, Sajid Javed, Maryam Sultana, Soon Ki Jung
2019 Neural Networks  
Convolutional neural networks, which are used in deep learning, have been recently and excessively employed for background initialization, foreground detection, and deep learned features.  ...  We then discuss the adequacy of deep neural networks for the task of background subtraction. Finally, experimental results are presented for the CDnet 2014 dataset.  ...  To address these limitations, since 2009, a robust PCA through decomposition into low-rank plus sparse matrices [32, 66, 67, 68, 69] has been widely used in the field.  ... 
doi:10.1016/j.neunet.2019.04.024 fatcat:tbr6uexzyvbpbeptbvuo7imrdu

Moving Objects Detection with a Moving Camera: A Comprehensive Review [article]

Marie-Neige Chapel, Thierry Bouwmans
2020 arXiv   pre-print
During about 30 years, a lot of research teams have worked on the big challenge of detection of moving objects in various challenging environments.  ...  For this purpose, we propose to classify these methods according to the choose of the scene representation: one plane or several parts.  ...  Acknowledgments This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  ... 
arXiv:2001.05238v1 fatcat:itswnqe3g5ey7c2apy35xqjjza

Table of contents

2020 IEEE Transactions on Image Processing  
Li 2301 Robust Low-Rank Tensor Minimization via a New Tensor Spectral k-Support Norm ...... J. Lou and Y.-M.  ...  Tao 4376 Robust Structural Low-Rank Tracking .................................... S. Javed, A. Mahmood, J. Dias, and N.  ... 
doi:10.1109/tip.2019.2940372 fatcat:h23ul2rqazbstcho46uv3lunku

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 9084-9098 Grayscale-Thermal Tracking via Inverse Sparse Representation-Based Collaborative Encoding.  ...  Li, X., +, TIP 2020 2139-2149 Grayscale-Thermal Tracking via Inverse Sparse Representation-Based Col- laborative Encoding.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

A Method for Infrared Sea-sky Condition Judgment and Search System: Robust Target Detection via PLS and CEDoG

Dongdong Ma, Lili Dong, Wenhai Xu
2020 IEEE Access  
Comparing with the other state-of-the-art methods in the experiments, our strategy has a robust and effective performance in terms of recall, precision, elapsed time, complexity, detection and false alarm  ...  INDEX TERMS Judge sea-sky background, maritime surveillance, infrared target detection.  ...  Wavelet denoising is based on haar's wavelet base to perform wavelet decomposition of the original image to obtain low-frequency images.  ... 
doi:10.1109/access.2020.3047736 fatcat:ndqr5ednsfcv3bg45gs3tdfzgu

Aerial Video Trackers Review

Jinlu Jia, Zhenyi Lai, Yurong Qian, Ziqiang Yao
2020 Entropy  
It defines the relationship between the target detection tracking box and the weight of the new particles.  ...  After the early association, two detection subsets are used for tracking.  ... 
doi:10.3390/e22121358 pmid:33266268 pmcid:PMC7761283 fatcat:xlrcyulsjzeq7bjxe3fgmnuhrq
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