Acceleration of object tracking in high‐speed videogrammetry using a parallel OpenMP and SIMD strategy

Tong, Xiaohua; Zheng, Shouzhu; Gao, Su; Chen, Peng; Guo, Chengcheng; Liu, Shijie; Xie, Huan; Jin, Yanmin; Ye, Zhen; Wei, Chao; Hu, Lifen

doi:10.1111/phor.12279

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…2) Compared Methods: We compared the performance of the proposed method with three state-of-the-art methods, which are the centroid search algorithm [17], Arc-Support [24] and AAMED (Arc Adjacency Matrix-Based Ellipse Detector) [25]. The centroid search algorithm integrated into the PhotoModeler® Scanner software can achieve high-precision positioning of marks, which is currently widely used in videogrammetry [2,22,23]. Arc-Support utilized rich geometric features and arc-support line segments to complete the ellipse detection tasks.…”

Section: Comparison Of Center Identification 1) Evaluation Metricsmentioning

confidence: 99%

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A High-Precision Recognition Method of Circular Marks Based on CMNet Within Complex Scenes

Hong

Tong

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate recognition of circular marks is crucial for calibration, object tracking, and threedimensional reconstruction in videogrammetry. However, most existing studies were designed under single or relatively simple scenes. When the existing algorithms are applied to more complex scenarios, it will result in higher false detection and miss-detection rate. In this article, we present a high-precision recognition method based on a novel deep learning model, Circular-MarkNet (CMNet) to solve this problem. The proposed network consists of three main steps: first, circular marks are detected using the improved YOLOv4 model to narrow the search region of the circular contour; the contour of the circular marks is then extracted based on the saliency object detection model BASNet; and finally, least square fitting (LSF) is used to calculate the central pixel coordinate of the identified contour on the saliency map. The proposed method was tested under three complex scenarios with different characteristics and disturbances. The experimental results demonstrated that: (1) the proposed CMNet can effectively recognize of circular marks within complex scenes, which reveals the superiority and generalization ability of the proposed method; (2) the improved YOLOv4 can significantly enhance the detection accuracy of circular marks, which is crucial to the subsequent saliency courter detection and circle center identification; (3) Circular-MarkNet achieved the best performance, with an RMSE of 0.0713 pixel, compared to the state-of-the-art methods.

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Section: Comparison Of Center Identification 1) Evaluation Metricsmentioning

confidence: 99%

“…For noncoded marks, the detection relied on semi-automated and ellipse detection. Semi-automated may require the operator to box a search region for the locations of targets [2,22,23]. The ellipse detection methods [24,25] used circle edge geometric features to complete the related detection tasks.…”

mentioning

confidence: 99%

A High-Precision Recognition Method of Circular Marks Based on CMNet Within Complex Scenes

Hong

Tong

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…NCC is frequently used to estimate the similarity between matching entities in two images due to its simplicity and reliability [51]. For a patch r(x, y) in the reference image and a corresponding patch t(x + m, y + n) in the template image, where m and n stand for the offsets within the search range, the NCC between two patches is defined as [52]:…”

Section: Similarity Measuresmentioning

confidence: 99%

Area-Based Dense Image Matching with Subpixel Accuracy for Remote Sensing Applications: Practical Analysis and Comparative Study

Xu²,

Chen

et al. 2020

Remote Sensing

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Dense image matching is a crucial step in many image processing tasks. Subpixel accuracy and fractional measurement are commonly pursued, considering the image resolution and application requirement, especially in the field of remote sensing. In this study, we conducted a practical analysis and comparative study on area-based dense image matching with subpixel accuracy for remote sensing applications, with a specific focus on the subpixel capability and robustness. Twelve representative matching algorithms with two types of correlation-based similarity measures and seven types of subpixel methods were selected. The existing matching algorithms were compared and evaluated in a simulated experiment using synthetic image pairs with varying amounts of aliasing and two real applications of attitude jitter detection and disparity estimation. The experimental results indicate that there are two types of systematic errors: displacement-dependent errors, depending on the fractional values of displacement, and displacement-independent errors represented as unexpected wave artifacts in this study. In addition, the strengths and limitations of different matching algorithms on the robustness to these two types of systematic errors were investigated and discussed.

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“…However, the scale of targets in the visual measurement system will continue to change during long-distance movement (Benninghoff et al, 2014;Zhao et al, 2021). The commonly used tracking algorithms, such as least-squares matching (LSM) (Ackermann, 1984;Tong et al, 2019) and feature-based methods (Mohammadi et al, 2022;Ye et al, 2018Ye et al, , 2019, are no longer suitable for this type of experimental scene. Therefore, this paper adopts a simple but effective scale-adaptive tracking method.…”

Section: Introductionmentioning

confidence: 99%

Dynamic measurement of a long‐distance moving object using multi‐binocular high‐speed videogrammetry with adaptive‐weighting bundle adjustment

Tong,

Gao,

et al. 2024

The Photogrammetric Record

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The dynamic measurement of position and attitude information of a long‐distance moving object is a common demand in ground testing of aerospace engineering. Due to the movement from far to near and the limitations of camera resolution, it is necessary to use multi‐binocular cameras for segmented observation at different distances. However, achieving accurate and continuous position and attitude estimation is a challenging task. Therefore, this paper proposes a dynamic monitoring technique for long‐distance movement based on a multi‐binocular videogrammetric system. Aiming to solve the problem that the scale in images changes constantly during the moving process, a scale‐adaptive tracking method of circular targets is presented. Bundle adjustment (BA) with joint segments using an adaptive‐weighting least‐squares strategy is developed to enhance the measurement accuracy. The feasibility and reliability of the proposed technique are validated by a ground testing of relative measurement for spacecraft rendezvous and docking. The experimental results indicate that the proposed technique can obtain the actual motion state of the moving object, with a positioning accuracy of 3.2 mm (root mean square error), which can provide a reliable third‐party verification for on‐orbit measurement systems in ground testing. Compared with the results of BA with individual segments and vision measurement software PhotoModeler, the accuracy is improved by 45% and 30%, respectively.

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Acceleration of object tracking in high‐speed videogrammetry using a parallel OpenMP and SIMD strategy

Cited by 5 publications

References 30 publications

A High-Precision Recognition Method of Circular Marks Based on CMNet Within Complex Scenes

A High-Precision Recognition Method of Circular Marks Based on CMNet Within Complex Scenes

Area-Based Dense Image Matching with Subpixel Accuracy for Remote Sensing Applications: Practical Analysis and Comparative Study

Dynamic measurement of a long‐distance moving object using multi‐binocular high‐speed videogrammetry with adaptive‐weighting bundle adjustment

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