Grayscale-Thermal Object Tracking via Multitask Laplacian Sparse Representation

Li, Chenglong; Sun, Xiang; Wang, Xiao; Zhang, Lei; Tang, Jin

doi:10.1109/tsmc.2016.2627052

Cited by 76 publications

(28 citation statements)

References 28 publications

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“…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%

“…For example, Liu et al [25] performed joint sparse representation calculation on both grayscale and thermal modalities and performed online tracking in Bayesian filtering framework. Li et al [26] utilized the multitask Laplacian sparse representation and integrated modal reliabilities into the model to achieve effective fusion. In [3], they proposed a patch-based graph model to learn object feature presentation for RGB-T tracking, where the graph is optimized via weighted sparse representations that utilize multi-modality information adaptively.…”

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

156

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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%

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

156

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“…RGBT tracking receives much attention recently and becomes more and more popular [17,21,22,16,20,23]. Recent works [21,17,16] employ reconstruction residues [21,17] or classification scores [16] to guide the weights learning of modalities to achieve adaptive fusion of RGB and thermal modalities. However, these methods tend to lose target objects in tracking process when the reconstruction residues or classification scores are unreliable in representing modal reliabilities.…”

Section: Rgbt Tracking Methodsmentioning

confidence: 99%

Multi-Adapter RGBT Tracking

Zheng

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

Self Cite

143

116

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The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing works focus on modality-specific information integration by introducing modality weights to achieve adaptive fusion or learning robust feature representations of different modalities. Although these methods could effectively deploy the modality-specific properties, they ignore the potential values of modality-shared cues as well as instance-aware information, which are crucial for effective fusion of different modalities in RGBT tracking. In this paper, we propose a novel Multi-Adapter convolutional Network (MANet) to jointly perform modality-shared, modality-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. We design three kinds of adapters within our network. In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object. Moreover, to reduce computational complexity for real-time demand of visual tracking, we design a parallel structure of generic adapter and modality adapter. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against other state-ofthe-art RGB and RGBT tracking algorithms.

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“…In the past decade, numerous TIR pedestrian tracking methods have been proposed to solve various challenges. Similar to visual object tracking [17]- [25] and grayscalethermal tracking [26], there are two categories of TIR pedestrian trackers: generative and discriminative. Generative TIR pedestrian trackers focus on the modeling of the pedestrian's appearance at current frame and search for the most similar candidates in next frame.…”

Section: B Tir Pedestrian Tracking Methodsmentioning

confidence: 99%

PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark

Liu¹,

2020

IEEE Trans. Multimedia

109

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Thermal infrared (TIR) pedestrian tracking is one of the important components among numerous applications of computer vision, which has a major advantage: it can track pedestrians in total darkness. The ability to evaluate the TIR pedestrian tracker fairly, on a benchmark dataset, is significant for the development of this field. However, there is not a benchmark dataset. In this paper, we develop a TIR pedestrian tracking dataset for the TIR pedestrian tracker evaluation. The dataset includes 60 thermal sequences with manual annotations. Each sequence has nine attribute labels for the attribute based evaluation. In addition to the dataset, we carry out the largescale evaluation experiments on our benchmark dataset using nine publicly available trackers. The experimental results help us understand the strengths and weaknesses of these trackers. In addition, in order to gain more insight into the TIR pedestrian tracker, we divide its functions into three components: feature extractor, motion model, and observation model. Then, we conduct three comparison experiments on our benchmark dataset to validate how each component affects the tracker's performance. The findings of these experiments provide some guidelines for future research.Index Terms-thermal infrared, pedestrian tracking, benchmark, dataset Q. Liu, Z. He (Corresponding author) and X. Li are with the

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Grayscale-Thermal Object Tracking via Multitask Laplacian Sparse Representation

Cited by 76 publications

References 28 publications

RGB-T Image Saliency Detection via Collaborative Graph Learning

RGB-T Image Saliency Detection via Collaborative Graph Learning

Multi-Adapter RGBT Tracking

PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark

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