Labeled Multi‐Bernoulli Maneuvering Target Tracking Algorithm via TSK Iterative Regression Model

WANG, Xiaoli; Xie, Weixin; LI, Liangqun

doi:10.1049/cje.2020.00.156

Cited by 8 publications

(5 citation statements)

References 35 publications

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“…With the invention of machine learning models such as Support Vector Machines [10] and Deep Neural Networks [11], many artificial intelligence tasks are mainly done in feature engineering. Feature engineering refers to the process of using domain-related knowledge to study the data feature representation that can maximize the performance of machine learning algorithms.…”

Section: Application Methods Of Multitarget Tracking Algorithm Based ...mentioning

confidence: 99%

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Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget Tracking Algorithm

Song

Zhao

2022

Computational Intelligence and Neuroscience

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Freestyle skiing U-shaped field is a snow sport that uses double boards to perform a series of action skills in a U-shaped pool, which requires very high skills for athletes. In this era of deep learning, in order to develop a more scientific training method, this paper combines multitarget tracking algorithm and deep learning to conduct research in freestyle skiing U-shaped venue skills motion capture. Therefore, this paper combines the convolutional neural network and multitarget tracking algorithm in deep learning to study the human action recognition technology, and then uses the LSTM module to study the freestyle skiing U-shaped venue skills. Finally, this paper designs the training method of the action recognition algorithm of the freestyle U-shaped skiing skills multitarget tracking algorithm based on deep learning. This paper also designs multitarget tracking dataset experiments and model updating experiments. Based on the data of experimental analysis, the training method designed in this paper is optimized, and finally compared with the traditional training method. Compared with the traditional freestyle U-shaped skiing skills training method, the experimental results show that the training method of the freestyle U-shaped skiing skills multitarget tracking algorithm action recognition algorithm is based on deep learning designed in this paper and this improves the skill score by 14.48%. Most professional students are very satisfied with the training method designed in this paper.

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Section: Application Methods Of Multitarget Tracking Algorithm Based ...mentioning

confidence: 99%

“…Filters can be applied to image areas. It usually uses multiple filters simultaneously [10]. For example, apply a 6 × 6 filter to an image.…”

Section: Application Methods Of Multitarget Tracking Algorithm Based ...mentioning

confidence: 99%

Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget Tracking Algorithm

Song

Zhao

2022

Computational Intelligence and Neuroscience

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“…From an image processing perspective, target tracking involves continuously detecting and tracking a specific object in a sequence of images to determine its motion trajectory (Wang et al, 2022). This process requires estimating the object's state, which is uncertain due to noise in the observations.…”

Section: Multi-target Real-time Tracking Algorithmmentioning

confidence: 99%

Application of Multimedia Man-Machine Interaction in College Physical Education Teaching

Zhu,

Tian,

2024

Journal of Cases on Information Technology

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In the realm of physical education in college, the traditional classroom teaching approach primarily relies on paper textbooks and demonstrations by the teacher. However, in today's physical education classrooms, there is a need to change these traditional teaching methods. Utilizing multimedia technology and interactive interfaces, teachers can demonstrate the essential actions and intricate details through multimedia videos and other formats such as push-pull and shaking. This chapter introduces a multi-segment human body tracking algorithm that focuses on real-time tracking. The human body target is divided into multiple segments, and an online learning method based on Hough Forests is used to learn the overall appearance of the human body and the appearance model of each segment. The research findings indicate that when the merged area exceeds 82.36% of the pre-segmentation area using watershed analysis, smaller areas lead to faster segmentation speed, while larger areas result in slower segmentation speed. Compared to other methods, this approach yields better recognition results.

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“…The multi-target Bayesian (MTB) filter provides an optimal solution for the RFS-based MTT algorithms. However, due to the existence of multiple integrals, as well as the combinatorial nature stemming from the infinite-dimensional multi-target densities, the MTB filter is computationally intractable, which limits its application [1,2,4]. Recently, various suboptimal solutions have been successively developed, with two of the most well known being the probability hypothesis density (PHD) filter [1,5] and the multi-Bernoulli (MeMBer, MB) filter [6].…”

Section: Introductionmentioning

confidence: 99%

Student’s t-Based Robust Poisson Multi-Bernoulli Mixture Filter under Heavy-Tailed Process and Measurement Noises

Zhu,

Xie,

Liu

2023

Remote Sensing

Self Cite

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A novel Student’s t-based robust Poisson multi-Bernoulli mixture (PMBM) filter is proposed to effectively perform multi-target tracking under heavy-tailed process and measurement noises. To cope with the common scenario where the process and measurement noises possess different heavy-tailed degrees, the proposed filter models this noise as two Student’s t-distributions with different degrees of freedom. Furthermore, this method considers that the scale matrix of the one-step predictive probability density function is unknown and models it as an inverse-Wishart distribution to mitigate the influence of heavy-tailed process noise. A closed-form recursion of the PMBM filter for propagating the approximated Gaussian-based PMBM posterior density is derived by introducing the variational Bayesian approach and a hierarchical Gaussian state-space model. The overall performance improvement is demonstrated through three simulations.

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Labeled Multi‐Bernoulli Maneuvering Target Tracking Algorithm via TSK Iterative Regression Model

Cited by 8 publications

References 35 publications

Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget Tracking Algorithm

Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget Tracking Algorithm

Application of Multimedia Man-Machine Interaction in College Physical Education Teaching

Student’s t-Based Robust Poisson Multi-Bernoulli Mixture Filter under Heavy-Tailed Process and Measurement Noises

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