Aircraft tracking based on fully conventional network and Kalman filter

Yang, Jiachen; Zhao, Weirong; Han, Yurong; Ji, Chunqi; Jiang, Bin; Zheng, Zhihui; Song, Houbing

doi:10.1049/iet-ipr.2018.5022

Cited by 13 publications

(5 citation statements)

References 42 publications

(43 reference statements)

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“…Yang et al proposed a parallax estimation network, which only generates depth maps for target candidate regions, learned the shape prior of feature categories to reduce the generation error of depth maps, and finally used depth maps to solve the target localization problem, which improves the detection. Sun proposed a parallax estimation network to generate depth maps only for the target candidate regions and learned the shape prior of the feature class to reduce the generation error [18]. e online multitarget tracking algorithm is proposed based on the constructed 3D target detection network and combined with the optimal recursive algorithm Kalman 2 Complexity filtering to address the problem of false and missed detection.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Duan

2021

Complexity

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The effect is tested in various specific scenes of sports videos to complete the multitarget motion multitarget tracking detection application applicable to various specific scenes within sports videos. In this paper, deep neural networks are applied to sports video multitarget motion shadow suppression and accurate tracking to improve tracking performance. After the target frame selection is determined, the tracker uses an optical flow method to estimate the limits of the target sports video multitarget motion based on the sports video multitarget motion of the target object between frames. The detector first scans each sports video image frame one by one, observing the previously discovered and learned image frame subregions one by one until the current moment that is highly like the target to be tracked. The preprocessed remote sensing images are converted into grayscale images, the histogram is normalized, and the appropriate height threshold is selected in combination with the regional growth function to realize the rejection of sports video multitarget motion shadow and establish the sports video multitarget network model. The distance and direction of the precise target displacement are determined by frequency-domain vectors and null domain vectors, and the target action judgment mechanism is formed by decision learning. Finally, comparing with other shadow rejection and precision tracking algorithms, the proposed algorithm achieves greater advantages in terms of accuracy and time consumption.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Duan

2021

Complexity

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“…A slightly unconventional mechanism of object tracking is carried out by Yang et al [21] considering the use case of tracking aircraft. The study has used a deep learning mechanism to improvise the accuracy in the tracking system where the model integrates the Kalman filter and extended Kalman filter to forecast the trajectory.…”

Section: A Kalman Filter-based Methodsmentioning

confidence: 99%

Compact Scrutiny of Current Video Tracking System and its Associated Standard Approaches

Kumar¹,

Kavya²

2020

IJACSA

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“…When the neural network of the filter is used to extract the features, the optimal weights can be obtained without iterative operation; thus, the calculation time is reduced [17]. In order to further discuss the advantages of PCANet depth network in extracting the features of high-resolution animated cartoon video images, a mapping eigenvalue of N, 8 × 8 and 1 moving distance is set up for the input of animated video images [18,19]. e main parts of the mapping features L 1 were computed using the PCANet algorithm, and a two-layer filter filters L 1 .…”

Section: Deep Feature Extraction Model Based On Deep Learningmentioning

confidence: 99%

Animation Design Based on 3D Visual Communication Technology

Shan

Wang

2022

Scientific Programming

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The depth synthesis of image texture is neglected in the current image visual communication technology, which leads to the poor visual effect. Therefore, the design method of film and TV animation based on 3D visual communication technology is proposed. Collect film and television animation videos through 3D visual communication content production, server processing, and client processing. Through stitching, projection mapping, and animation video image frame texture synthesis, 3D vision conveys animation video image projection. In order to ensure the continuous variation of scaling factors between adjacent triangles of animation and video images, the scaling factor field is constructed. Deep learning is used to extract the deep features and to reconstruct the multiframe animated and animated video images based on visual communication. Based on this, the frame feature of video image under gray projection is identified and extracted, and the animation design based on 3D visual communication technology is completed. Experimental results show that the proposed method can enhance the visual transmission of animation video images significantly and can achieve high-precision reconstruction of video images in a short time.

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Aircraft tracking based on fully conventional network and Kalman filter

Cited by 13 publications

References 42 publications

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Compact Scrutiny of Current Video Tracking System and its Associated Standard Approaches

Animation Design Based on 3D Visual Communication Technology

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