Infrared Target Tracking Algorithm Based on Algorithm Fusion

Zhiqiang, 管志强 Guan; Qian, 陈钱 Chen; Weixian, 钱惟贤 Qian; Yongsheng, 胡永生 Hu

doi:10.3788/aos20082805.0860

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, based on template matching and Kalman filtering [34], histogram and moment invariance [35], and particle filtering [30], some traditional infrared target tracking algorithms have been made great development. However, due to the human subjective intervention and preset features which have a large impact on the robustness and generalization ability of such algorithms, they are only applicable to tracking tasks in certain scenarios.…”

Section: Introductionmentioning

confidence: 99%

SiamMCA: Infrared Target Tracking Algorithm with Mixed Channel Attention

Li,

Diao,

Fang

et al. 2023

Preprint

View full text Add to dashboard Cite

Due to the imaging specificity of infrared images, compared with the RGB images, there are drawbacks such as blurring, low contrast, unclear texture details and large background noise. So the tracking is more difficult and challenging. There are two main methods for infrared target tracking. One is to use Handcrafted features, but it is time-consuming and costly. The other is infrared target tracking algorithm which is the current mainstream algorithm and is to apply the tracking model used for RGB tracking to infrared target tracking. Compared with the RGB images, the image features extracted from infrared images often have low quality and poor tracking performance. Therefore, to address these problems, based on the idea of Siamese network, an efficient infrared target tracking model SiamMCA is designed in this paper. SiamMCA is mainly composed of a feature extraction network module, a mixed channel feature enhancement module, a multi-scale feature fusion module and a regional regression network module (RPN) for object classification and bounding box regression. Firstly, the feature extraction network module based on the deep residual network Resnet50 extracts richer and more comprehensive feature information by introducing multi-head self-attention. Then the mixed channel feature enhancement module assigns different weights to the features of different channels, which makes the model more focus on the regions of interest. Finally, response maps with multi-scale features are obtained by a multi-scale feature fusion module and are fed into the regional regression network module to classify the target and predict and regress the bounding box. The experimental results show that SiamMCA achieves nice performance on infrared data benchmarks LSOTB-TIR and PTB-TIR, and can effectively cope with various infrared target tracking tasks.

show abstract

Section: Introductionmentioning

confidence: 99%

SiamMCA: Infrared Target Tracking Algorithm with Mixed Channel Attention

Li,

Diao,

Fang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Moving vehicle detection and tracking using monocular vision

Miao

Shen

et al. 2014

IJVS

View full text Add to dashboard Cite

Image fusion algorithm based on block-DCT in wavelet domain

Tian¹,

Feng²,

Shou-Ping³

et al. 2011

Acta Phys. Sin.

View full text Add to dashboard Cite

A feature-level image fusion algorithm using wavelet transform combined with the discrete cosine transform (DCT) is proposed. The basic idea is to perform block-DCT of each source image first, and then to select the retained coefficients according to maximum variance; each image is compressed to 25 percent of the original size. The coordinates of the retained coefficients are used as private keys. Finally, the processed DCT coefficient matrixes are taken as the wavelet coefficients and the fused image is obtained by taking the inverse wavelet transform. The experimental results show that the algorithm realizes the fusion of images with different sizes. Furthermore, one single image can be reconstructed by one single private key.

show abstract

Infrared Target Tracking Algorithm Based on Algorithm Fusion

Cited by 3 publications

References 0 publications

SiamMCA: Infrared Target Tracking Algorithm with Mixed Channel Attention

SiamMCA: Infrared Target Tracking Algorithm with Mixed Channel Attention

Moving vehicle detection and tracking using monocular vision

Image fusion algorithm based on block-DCT in wavelet domain

Contact Info

Product

Resources

About