A Novel Feature Points Tracking Algorithm in Terms of IMU-Aided Information Fusion

Guan, Qi; Wei, Guoliang; Wang, Licheng; Song, Yan

doi:10.1109/tii.2020.3024079

Cited by 12 publications

(2 citation statements)

References 27 publications

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“…Feature Extraction. Feature extraction is one of the core functions of the system [9]. Every time a piece of audio data is added to the audio library, its audio features should be extracted.…”

Section: Extraction Of Nonpresentation Attribute Informationmentioning

confidence: 99%

University Media Content Detection and Classification Based on Information Fusion Algorithm

Zhang

Yang

et al. 2022

Advances in Multimedia

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In order to further solve the problems in promoting the classification of media content in colleges and universities, the effective analysis and understanding of multimedia data content can be better realized based on the characteristics of multimedia data in colleges and universities, combining with the characteristics of rich information, large differences in performance, and large amount of large-scale data. This essay mainly introduces the technology of university media content detection and classification based on information fusion algorithm and focuses on the application of university multimedia content detection, analysis, and understanding, to explore the image discrimination auxiliary attribute feature learning and content association prediction and classification. A benchmark model for media content detection and classification is constructed. Through the model test, it is found that the F 1 value of the model is more than 70%, the check rate is more than 80%, and the recall rate is more than 50%. On this basis, a content detection system based on campus network is constructed.

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Section: Extraction Of Nonpresentation Attribute Informationmentioning

confidence: 99%

University Media Content Detection and Classification Based on Information Fusion Algorithm

Zhang

Yang

et al. 2022

Advances in Multimedia

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show abstract

“…Through simulating the interactions among the individuals in the fish schooling or bird flocking, the particle swarm optimization (PSO) algorithm has been presented in [16] with the purpose of exploring the searching space, which is made possible by automatically adjusting the current velocities and the current positions of the particles according to the competition and cooperation among the particles. Serving as a powerful evolutionary technique, the PSO algorithm is capable of discovering the globally optimal solution in an efficient yet effective way in the research areas of parameter optimization, neural network training, clustering analysis, combination optimization, pattern recognition, image processing and so forth [1,5,6,8,17,20,22,30,32,36,41,46,50].…”

Section: Introductionmentioning

confidence: 99%

A novel PID-like particle swarm optimizer: on terminal convergence analysis

Wang

Han

et al. 2021

Complex Intell. Syst.

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In this paper, a novel proportion-integral-derivative-like particle swarm optimization (PIDLPSO) algorithm is presented with improved terminal convergence of the particle dynamics. A derivative control term is introduced into the traditional particle swarm optimization (PSO) algorithm so as to alleviate the overshoot problem during the stage of the terminal convergence. The velocity of the particle is updated according to the past momentum, the present positions (including the personal best position and the global best position), and the future trend of the positions, thereby accelerating the terminal convergence and adjusting the search direction to jump out of the area around the local optima. By using a combination of the Routh stability criterion and the final value theorem of the Z-transformation, the convergence conditions are obtained for the developed PIDLPSO algorithm. Finally, the experiment results reveal the superiority of the designed PIDLPSO algorithm over several other state-of-the-art PSO variants in terms of the population diversity, searching ability and convergence rate.

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Lightweight omnidirectional visual-inertial odometry for MAVs based on improved keyframe tracking and marginalization

Gao,

Lian,

Tang

2024

Telecommun Syst

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A Novel Feature Points Tracking Algorithm in Terms of IMU-Aided Information Fusion

Cited by 12 publications

References 27 publications

University Media Content Detection and Classification Based on Information Fusion Algorithm

University Media Content Detection and Classification Based on Information Fusion Algorithm

A novel PID-like particle swarm optimizer: on terminal convergence analysis

Lightweight omnidirectional visual-inertial odometry for MAVs based on improved keyframe tracking and marginalization

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