Tracking Registration Algorithm for Augmented Reality Based on Template Tracking

Cao, Peng-Xia; Li, Wenxin; Ma, Weiping

doi:10.1007/s11633-019-1198-3

Cited by 13 publications

(4 citation statements)

References 22 publications

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“…This kind of light effect is generally successful under an imaginary light source. The similarity algorithm between target features and visual features can be applied here [22].…”

Section: Dynamic Target Detection and Tracking Technologymentioning

confidence: 99%

[Retracted] Badminton Path Tracking Algorithm Based on Computer Vision and Ball Speed Analysis

Lyu

Zhang

2021

Journal of Sensors

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With the development of artificial intelligence and the rapid development of the computer industry, the practicability of computer vision programs is gradually improved. In this paper, the badminton path tracking algorithm based on computer vision analyzes the badminton trajectory and speed. This paper is aimed at analyzing the image processing technology and path tracking algorithm by using computer vision to obtain relevant data and then exploring the factors of badminton path and ball speed transformation, which provides reference significance for badminton players in future training. The path tracking algorithm is used to predict the rotation angle, the ball speed, and the athlete’s body information during the badminton movement through sensors, and the position information of the moving target is captured based on the visual field tracking and target dynamic tracking. Combined with specific badminton players, we first analyze the angle of each limb and the speed of the racket in the process of movement and record the data. Determine different positioning points for different actions, such as pushing the ball, picking the ball, hooking the ball, and rubbing the hair. In this process, we aim at the connection between the highest point and the lowest point of the badminton trajectory and the ball speed. This process fully combines the theoretical knowledge of the path tracking algorithm. The experimental results show that different service skills have different effects on the trajectory and speed of badminton. In the test of relevant data by using the push and receive skills, the lowest point of the ball served by player A in the first three times is higher than that by player B. The most significant difference between the lowest points of the five times is the second time, with a difference of 0.2 m, and the third time, with a minimum difference of 0.03 m.

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“…This kind of light effect is generally successful under an imaginary light source. The similarity algorithm between target features and visual features can be applied here [22].…”

Section: Dynamic Target Detection and Tracking Technologymentioning

confidence: 99%

[Retracted] Badminton Path Tracking Algorithm Based on Computer Vision and Ball Speed Analysis

Lyu

Zhang

2021

Journal of Sensors

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“…The vision-based class can be further separated into two sub-classes: marker-based and marker-less methods (Cao et al, 2019). The first sub-class utilizes 2D images, such as markers, which the AR system detects to enable a device to determine its relative position and orientation to the marker and then project the virtual model according to the location of that marker in the real world and its reference in the virtual world (M. Chu et al, 2018), as seen in Yan (2019).…”

Section: Related Work Registration Methodsmentioning

confidence: 99%

Keeping up with the Code - Communicating the Decision Making History of Architectural Scripts

Hamilton¹,

Butler²,

Gardner³

et al. 2020

eCAADe Proceedings

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“…Optical flow method used to extract optical flow field in video or continuous images and can also be used to approximate motion under certain conditions such as moving target tracking.The typical methods are Horn-Schunck algorithm [5] and Lucas-Kanade algorithm [6]. The difference is that Horn-Schunck algorithm uses a global method to estimate the dense optical flow field of the image, that is, to calculate the optical flow for each pixel in the image.…”

Section: Optical Flowmentioning

confidence: 99%

Target localization network based on optical flow for object tracking

Chen

et al. 2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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Object tracking is a very challenging branch in the field of computer vision and plays an extremely important role in different fields. However, most algorithms still have no good robustness when the target is out of view, fast motion and low resulution. Therefore, this paper proposes a method based on siamese architecture and optical flow to address these situations. The tracking framework is mainly divided into tracking network and target localization network based on optical flow. The tracking network uses SiamBAN and the target localization network uses LiteFlowNet3 to estimate the optical flow information of target. The proposed method in this paper expands the search area of SiamBAN, and uses the target localization network to reposition the target when tracking fails. The target localization network can be easily embed into the existing siamese network to provide more accurate location of target during the tracking process. Experiments results on VOT2016, VOT2018 show that this method has good robustness against extreme conditions.

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Tracking Registration Algorithm for Augmented Reality Based on Template Tracking

Cited by 13 publications

References 22 publications

[Retracted] Badminton Path Tracking Algorithm Based on Computer Vision and Ball Speed Analysis

[Retracted] Badminton Path Tracking Algorithm Based on Computer Vision and Ball Speed Analysis

Keeping up with the Code - Communicating the Decision Making History of Architectural Scripts

Target localization network based on optical flow for object tracking

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