Siamese network with bidirectional feature pyramid for small target tracking

Liu, Lei; Kong, Guangqian; Duan, Xun; Long, Huiyun

doi:10.1117/1.jei.30.5.053028

Cited by 5 publications

(3 citation statements)

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“…SiamST 16 proposed a Siamese network with spatiotemporal awareness. Liu et al 17 proposed a small target tracking algorithm based on a bidirectional feature pyramid fusion framework. However, the pyramid method only works with predefined image scales.…”

Section: Visual Object Tracking Based On Siamese Networkmentioning

confidence: 99%

Siamese network with contrastive learning and adaptive template updating for object tracking

Cui,

Duan,

Kong

et al. 2023

J. Electron. Imag.

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Visual object tracking is a crucial task across numerous computer vision applications. However, object tracking algorithms face significant challenges stemming from deformation and fast motion, which frequently incur dramatic changes to the target's appearance. To address this problem, we propose a Siamese-network-based object tracking method that combines contrastive learning and adaptive template updating. First, we designed a contrastive branch based on a Siamese network, which constructs a contrastive network with a template branch. The objective of this network is to capture the invariance associated with different images of the same target. Subsequently, we implemented an adaptive template updating strategy for the timely capture of appearance changes in targets and adjusted the size and position of the bounding box. Finally, the α-complete intersection over union function was introduced to continuously optimize the generation of bounding boxes during training, guiding the model to produce more accurate tracking boxes and further improving the algorithm performance. The experimental results demonstrate that our algorithm achieves advanced performance on five datasets, namely GOT-10k, LaSOT, UAV123, OTB2015, and NFS.

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Section: Visual Object Tracking Based On Siamese Networkmentioning

confidence: 99%

Siamese network with contrastive learning and adaptive template updating for object tracking

Cui,

Duan,

Kong

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…20 Another is to use attention mechanism, multiscale feature fusion or other methods in convolutional neural networks to improve the quality of feature extraction. 21,22 In terms of image preprocessing, Huang and Du 23 denoised and filtered sequence images to extract static features and dynamic features of suspicious flame areas. However, these operations are time-consuming.…”

Section: Introductionmentioning

confidence: 99%

“…One is the image preprocessing method, which balances the brightness, contrast, and other indicators of the image through image enhancement technology so as to highlight the key information of the image 20 . Another is to use attention mechanism, multiscale feature fusion or other methods in convolutional neural networks to improve the quality of feature extraction 21 , 22 …”

Section: Introductionmentioning

confidence: 99%

Lightweight anti-interference flame detection method based on context information

Xue

Wang

et al. 2022

J. Electron. Imag.

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As an important part of fire prevention and control, there are high standards for realtime, anti-interference, and accuracy about flame detection. At present, flame target detection methods lack comprehensive research on the above three indicators. To solve this problem, we propose a lightweight anti-interference flame detection method based on improved YOLOv4tiny. On the ground of dynamic characteristics of flame changing with time, a double-stream structure of flame detection model is designed. First, deeply separable convolution is applied to YOLOv4-tiny, resulting in a lighter backbone network. Second, in the feature extraction stage, the learning ability of the network to shallow features is improved by further integrating multiscale features, and the efficient channel attention module is introduced into the feature pyramid network to further improve the accuracy. Finally, intersection over union (IOU) postprocessing algorithm is used to shield the interference of fire-like targets effectively. Experimental results show that the parameter quantity of our methods are 3.95 and 4.22 MB, respectively, and accordingly their precisions reach 94.10% and 94.27%. Detection times are 35 and 46 ms, separately. In addition, after using IOU postprocessing algorithm, mAP and F1 composite index both increased to varying degrees, the consumption time is only 0.23 ms.

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Light-TBFNet: RGB-D salient detection based on a lightweight two-branch fusion strategy

Shi

Shen

Tan

et al. 2023

Multimed Tools Appl

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Siamese network with bidirectional feature pyramid for small target tracking

Cited by 5 publications

References 0 publications

Siamese network with contrastive learning and adaptive template updating for object tracking

Siamese network with contrastive learning and adaptive template updating for object tracking

Lightweight anti-interference flame detection method based on context information

Light-TBFNet: RGB-D salient detection based on a lightweight two-branch fusion strategy

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