Dr. Xiangtao Meng Profile

Dr. Xiangtao Meng

at China Academy of Aerospace Electronics Technology

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Publications (2)

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Proceedings Article | 18 December 2023 Paper

Real-time high-precision positioning and speed measurement algorithm for swimming based on YOLOv5 and DeepSort

Chaochao Zhang, He Zhao, Xiangtao Meng

Proceedings Volume 12967, 1296705 (2023) https://doi.org/10.1117/12.3005745

KEYWORDS: Target detection, Detection and tracking algorithms, Cameras, Calibration, Object detection, Signal filtering, Image processing, Distortion

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An swimmer's level and ability are often evaluated in swimming based on speed. Therefore, it is essential to quantify this speed accurately to guide their training. Swimming speed measurement methods are typically divided into two categories: using an LED as a reference object or wearing inertial navigation equipment. The former does not provide real-time feedback, while the latter can easily diverge. Through digital image processing and tracking, the speed can be accurately measured in real-time by analyzing the image's deep abstract features, frame by frame, even in complex and constantly changing sports scenes. To meet multiple swimmers' high-precision positioning and speed measurement requirements, YOLOv5 utilizes its advantages of full-view, low-latency, and high-precision multi-target recognition, making it well-suited for swimming speed measurement. DeepSort, on the other hand, leverages its powerful representation learning and accurate matching capability and, when combined with YOLOv5, can achieve real-time and precise tracking of multiple targets. Therefore, this paper proposes a real-time high-precision positioning and speed measurement algorithm for swimming based on YOLOv5 and DeepSort. The nine-point calibration method gets swimmers' positioning and speed information from swimmers' target boxes, which are tracked by YOLOv5 and DeepSort. The results of the multiple tests in the actual swimming competition scene show that the algorithm's tracking accuracy can reach more than 90%, and the positioning error of the first swimming lane is about 1.2cm. It has strong feasibility and engineering practicability.

Proceedings Article | 25 October 2016 Paper

Flutter signal extracting technique based on FOG and self-adaptive sparse representation algorithm

Jian Lei, Xiangtao Meng, Zheng Xiang

Proceedings Volume 10158, 101581J (2016) https://doi.org/10.1117/12.2247439

KEYWORDS: Fiber optic gyroscopes

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Due to various moving parts inside, when a spacecraft runs in orbits, its structure could get a minor angular vibration, which results in vague image formation of space camera. Thus, image compensation technique is required to eliminate or alleviate the effect of movement on image formation and it is necessary to realize precise measuring of flutter angle. Due to the advantages such as high sensitivity, broad bandwidth, simple structure and no inner mechanical moving parts, FOG (fiber optical gyro) is adopted in this study to measure minor angular vibration. Then, movement leading to image degeneration is achieved by calculation. The idea of the movement information extracting algorithm based on self-adaptive sparse representation is to use arctangent function approximating L₀ norm to construct unconstrained noisy-signal-aimed sparse reconstruction model and then solve the model by a method based on steepest descent algorithm and BFGS algorithm to estimate sparse signal. Then taking the advantage of the principle of random noises not able to be represented by linear combination of elements, useful signal and random noised are separated effectively. Because the main interference of minor angular vibration to image formation of space camera is random noises, sparse representation algorithm could extract useful information to a large extent and acts as a fitting pre-process method of image restoration. The self-adaptive sparse representation algorithm presented in this paper is used to process the measured minor-angle-vibration signal of FOG used by some certain spacecraft. By component analysis of the processing results, we can find out that the algorithm could extract micro angular vibration signal of FOG precisely and effectively, and can achieve the precision degree of 0.1".

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