Fast Grayscale-Thermal Foreground Detection With Collaborative Low-Rank Decomposition

Yang, Siwei; Luo, Bin; Li, Chenglong; Wang, Guizhao; Tang, Jin

doi:10.1109/tcsvt.2017.2721460

Cited by 25 publications

(5 citation statements)

References 28 publications

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“…There are several typical problems that use these two modalities. For the problem of grayscale-thermal foreground detection, Yang et al [24] proposed a collaborative low-rank decomposition approach to achieve cross-modality consistency, and also incorporated the modality weights to achieve adaptive fusion of multiple source data. Herein, grayscale-thermal is the special case of RGB-T, where grayscale denotes one-channel gray image.…”

Section: B Rgb-t Vision Methodsmentioning

confidence: 99%

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RGB-T Image Saliency Detection via Collaborative Graph Learning

Xia

et al. 2020

IEEE Trans. Multimedia

Self Cite

157

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Image saliency detection is an active research topic in the community of computer vision and multimedia. 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. Our approach relies on a novel collaborative graph learning algorithm. In particular, we take superpixels as graph nodes, and collaboratively use hierarchical deep features to jointly learn graph affinity and node saliency in a unified optimization framework. 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. Extensive experiments on the public dataset and the newly created dataset suggest that the proposed approach performs favorably against the state-of-the-art RGB-T saliency detection methods.

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Section: B Rgb-t Vision Methodsmentioning

confidence: 99%

“…Integrating RGB and thermal infrared data has drawn more attentions in the computer vision community [3]- [5], [24]- [26] with the popularity of thermal sensors. There are several typical problems that use these two modalities.…”

Section: B Rgb-t Vision Methodsmentioning

confidence: 99%

RGB-T Image Saliency Detection via Collaborative Graph Learning

Xia

et al. 2020

IEEE Trans. Multimedia

Self Cite

157

View full text Add to dashboard Cite

show abstract

“…In recent years, with the popularity of thermal sensors, integrating RGB and thermal infrared data has applied to many tasks of computer vision [19], [20], [21], [5], [22]. In addition to RGBT SOD, there are many methods adopting different modality to obtain multiple cues for better detection, such as RGBD SOD input with depth and RGB images.…”

Section: Multi-modal Sod Methodsmentioning

confidence: 99%

RGBT Salient Object Detection: A Large-scale Dataset and Benchmark

Tu¹,

Ma²,

Li³

et al. 2020

Preprint

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Salient object detection in complex scenes and environments is a challenging research topic. Most works focus on RGB-based salient object detection, which limits its performance of real-life applications when confronted with adverse conditions such as dark environments and complex backgrounds. Taking advantage of RGB and thermal infrared images becomes a new research direction for detecting salient object in complex scenes recently, as thermal infrared spectrum imaging provides the complementary information and has been applied to many computer vision tasks. However, current research for RGBT salient object detection is limited by the lack of a large-scale dataset and comprehensive benchmark. This work contributes such a RGBT image dataset named VT5000, including 5000 spatially aligned RGBT image pairs with ground truth annotations. VT5000 has 11 challenges collected in different scenes and environments for exploring the robustness of algorithms. With this dataset, we propose a powerful baseline approach, which extracts multi-level features within each modality and aggregates these features of all modalities with the attention mechanism, for accurate RGBT salient object detection. Extensive experiments show that the proposed baseline approach outperforms the stateof-the-art methods on VT5000 dataset and other two public datasets. In addition, we carry out a comprehensive analysis of different algorithms of RGBT salient object detection on VT5000 dataset, and then make several valuable conclusions and provide some potential research directions for RGBT salient object detection. Our new VT5000 dataset are made publicly available at https://pan.baidu.com/s/1O5TC-5sEya8N2EGm-xJ5mw Password:o57e.

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“…9. Some examples of works using this dataset for moving object detection using background subtraction approach are [177]- [179].…”

Section: ) Gtfd Datasetmentioning

confidence: 99%

A Comprehensive Survey of Video Datasets for Background Subtraction

Kalsotra

Arora

2019

IEEE Access

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Background 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 (IVA). In recent years, a number of video datasets intended for background subtraction have been created to address the problem of large realistic datasets with accurate ground truth. The use of these datasets enables qualitative as well as quantitative comparisons and allows benchmarking of different algorithms. Finding the appropriate dataset is generally a cumbersome task for an exhaustive evaluation of algorithms. Therefore, we systematically survey standard video datasets and list their applicability for different applications. This paper presents a comprehensive account of public video datasets for background subtraction and attempts to cover the lack of a detailed description of each dataset. The video datasets are presented in chronological order of their appearance. Current trends of deep learning in background subtraction along with top-ranked background subtraction methods are also discussed in this paper. The survey introduced in this paper will assist researchers of the computer vision community in the selection of appropriate video dataset to evaluate their algorithms on the basis of challenging scenarios that exist in both indoor and outdoor environments.INDEX TERMS Background model, background subtraction, challenges, datasets, deep neural networks, foreground, intelligent video analytics (IVA), video frames.

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Fast Grayscale-Thermal Foreground Detection With Collaborative Low-Rank Decomposition

Cited by 25 publications

References 28 publications

RGB-T Image Saliency Detection via Collaborative Graph Learning

RGB-T Image Saliency Detection via Collaborative Graph Learning

RGBT Salient Object Detection: A Large-scale Dataset and Benchmark

A Comprehensive Survey of Video Datasets for Background Subtraction

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