Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise

Zhang, Boxuan; Hou, Xianghao; Yang, Yixin; Zhou, Jianbo; Xu, Shengyuan

doi:10.3389/fphy.2023.1142400

Cited by 5 publications

(1 citation statement)

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“…Currently, it is difficult to monitor targets with high precision, specifically in four areas: 1) Radical variations in target appearance throughout the tracking task, including target rotation, illumination changes, scale changes, etc., 2) frequent occlusion of targets during tracking; 3) drifting tracking frame caused by interactive motion between targets. 4) poor image quality with unclear targets in complex backgrounds [6].…”

Section: Introductionmentioning

confidence: 99%

Transformer-based target tracking algorithm for space-based optoelectronic detection

Zhu,

Leng,

et al. 2023

Front. Phys.

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The target tracking by space-based surveillance systems is difficult due to the long distances, weak energies, fast speeds, high false alarm rates, and low algorithmic efficiencies involved in the process. To mitigate the impact of these difficulties, this article proposes a target tracking algorithm based on image processing and Transformer, which employs a two-dimensional Gaussian soft-thresholding method to reduce the image noise, and combines a Laplace operator-weighted fusion method to augment the image, so as to improve the overall quality of the image and increase the accuracy of target tracking. Based on the SiamCAR framework, the Transformer model in the field of natural language processing is introduced, which can be used to enhance the image features extracted from the backbone network by mining the rich temporal information between the initial and dynamic templates. In order to capture the information of the target’s appearance change in the temporal sequence, a template update branch is introduced at the input of the algorithm, which realizes the dynamic update of the templates by constructing a template memory pool, and selecting the best templates for the candidate templates in the memory pool using the cosine similarity-based selection, thus ensuring the robustness of the tracking algorithm. The experimental results that compared with the SiamCAR algorithm and the mainstream algorithms, the TrD-Siam algorithm proposed in this article effectively improves the tracking success rate and accuracy, addressing poor target tracking performance under space-based conditions, and has a good value of application in the field of optoelectronic detection.

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Section: Introductionmentioning

confidence: 99%