Robust thermal infrared tracking via an adaptively multi-feature fusion model

Shu, Xiu; Li, Qiao; Zhang, Xinming; He, Zhenyu

doi:10.1007/s00521-022-07867-1

Cited by 36 publications

(12 citation statements)

References 66 publications

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“…P ERSON ReID task aims to identify and retrieve target pedestrians captured by non-overlapping cameras. It is an important topic in the field of computer vision with a wide range of practical applications, including video surveillance, security, and smart cities [1], [2]. With the rapid development of deep learning, holistic person ReID is making great progress, and various methods have been proposed [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Hierarchical Attentive Feature Aggregation for Person Re-identification

Dong,

2023

Preprint

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Recent efforts on person re-identification have shown promising results by learning discriminative features via multi-branch network. To further boost feature discrimination, attention mechanism has also been extensively employed. However, the branches in existing models generally work independently, which may compromise the ability of mining diverse features. To mitigate this issue, a novel framework called Hierarchical Attentive Feature Aggregation (Hi-AFA) is proposed. In Hi-AFA, a hierarchical aggregation mechanism is applied to learn attentive features. The current feature map is not only fed into the next stage, but also aggerated into another branch, leading to hierarchical feature flows along depth and parallel branches. We also present a simple feature suppression operation and a lightweight dual attention module with only a few parameters to guide feature learning. By this manner, the branches could cooperate to mine richer and more diverse feature representations. We integrate the hierarchical aggregation and multi-granularity feature learning into a unified architecture that builds upon OSNet, resulting a resource-economical and effective person re-identification model. Extensive experiments on three public datasets, including Market-1501, DukeMTMC-reID, and CUHK03, are conducted to validate the effectiveness of the proposed method, and results show that state-of-the-art performance is achieved.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Hierarchical Attentive Feature Aggregation for Person Re-identification

Dong,

2023

Preprint

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“…The general thought for the CNN model is that it requires a large volume of training data for better classification performance [ 44 ]. We plotted the training sample (in %) and the OA accuracy curve for the PU.…”

Section: Discussionmentioning

confidence: 99%

Advancing Hyperspectral Image Analysis with CTNet: An Approach with the Fusion of Spatial and Spectral Features

Yadav,

Kumar,

Jalal

et al. 2024

Sensors

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Hyperspectral image classification remains challenging despite its potential due to the high dimensionality of the data and its limited spatial resolution. To address the limited data samples and less spatial resolution issues, this research paper presents a two-scale module-based CTNet (convolutional transformer network) for the enhancement of spatial and spectral features. In the first module, a virtual RGB image is created from the HSI dataset to improve the spatial features using a pre-trained ResNeXt model trained on natural images, whereas in the second module, PCA (principal component analysis) is applied to reduce the dimensions of the HSI data. After that, spectral features are improved using an EAVT (enhanced attention-based vision transformer). The EAVT contained a multiscale enhanced attention mechanism to capture the long-range correlation of the spectral features. Furthermore, a joint module with the fusion of spatial and spectral features is designed to generate an enhanced feature vector. Through comprehensive experiments, we demonstrate the performance and superiority of the proposed approach over state-of-the-art methods. We obtained AA (average accuracy) values of 97.87%, 97.46%, 98.25%, and 84.46% on the PU, PUC, SV, and Houston13 datasets, respectively.

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“…Wu [46] enhanced the tracking performance of the DCF base tracker for infrared aerial targets by integrating adaptive learning from the initial frame and interference suppression. Yuan et al [47] proposed a multi-feature fusion model to integrate manual features with deep features, enhancing the discriminative ability for TIR targets. Yang [48] proposed a finegrained feature extraction network to strengthen the tracker's ability to resistance against distractors.…”

Section: Deep Learning-based Tir Trackermentioning

confidence: 99%

“…Yuan et al. [47] proposed a multi‐feature fusion model to integrate manual features with deep features, enhancing the discriminative ability for TIR targets. Yang [48] proposed a fine‐grained feature extraction network to strengthen the tracker's ability to resistance against distractors.…”

Section: Related Workmentioning

confidence: 99%

AODiMP‐TIR: Anti‐occlusion thermal infrared targets tracker based on SuperDiMP

Ma,

Yang,

Chen

2024

IET Image Processing

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To address the issue of tracking drift and failures in thermal infrared (TIR) tracking tasks caused by target occlusion, this study proposes an anti‐occlusion TIR target tracker named AODiMP‐TIR. This approach involves an anti‐occlusion strategy that relies on target occlusion status determination and trajectory prediction. This enables the prediction of the target's current position when it is identified as occluded, ensuring swift recapture upon reappearance. A criterion is introduced for occlusion status determination based on the classification response map of SuperDiMP. Additionally, a trajectory mapping module designed to decouple target motion from camera motion is presented, enhancing the precision of trajectory prediction. Comparative experiments with other state‐of‐the‐art trackers are conducted on the large‐scale high‐diversity thermal infrared object tracking benchmark (LSOTB‐TIR), LSOTB‐TIR100, and thermal infrared pedestrian tracking benchmark (PTB‐TIR) datasets. The results indicate that the AODiMP‐TIR performs well across all three datasets, particularly exhibiting outstanding performance in occlusion sequences. Furthermore, ablation study experiments confirm the effectiveness of the anti‐occlusion strategy, occlusion determination criterion and trajectory mapping module.

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Robust thermal infrared tracking via an adaptively multi-feature fusion model

Cited by 36 publications

References 66 publications

WITHDRAWN: Hierarchical Attentive Feature Aggregation for Person Re-identification

WITHDRAWN: Hierarchical Attentive Feature Aggregation for Person Re-identification

Advancing Hyperspectral Image Analysis with CTNet: An Approach with the Fusion of Spatial and Spectral Features

AODiMP‐TIR: Anti‐occlusion thermal infrared targets tracker based on SuperDiMP

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