High-Resolution Image Acquisition System for Rice Pests

Yuanyuan, 刘媛媛 Liu; Yuehai, 章越海 Zhang; Guiying, 余桂英 Yu; Mingyue, 张明月 Zhang; Jianfeng, 霍剑锋 Huo; Baowu, 张宝武 Zhang

doi:10.3788/lop55.021006

Cited by 1 publication

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“…At 30 Hz, the accuracy of joint point recognition is 2 cm. The data set contains 594 action sequences, a total of 719,359 frames, human posture and joint data, a total of 6,144 actions, obtained by 12 actions performed by 30 people [13].…”

Section: A Aerobics Training Action Recognition Data Setmentioning

confidence: 99%

Retracted: Aided Image Acquisition System for Aerobics Training Based on Motion Recognition Technology

Hai-ying

Liu

2024

IEEE Access

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The image acquisition system is the main part of acquiring image information, and its performance largely determines the accuracy and difficulty of subsequent planning. The main purpose of this article is to design an image acquisition system for aerobics training based on motion recognition technology. This article mainly introduces the teaching experiments based on the design of aerobics courses. Through the empirical investigation and analysis of the impact of relevant experimental data on aviation training courses, attempts to establish the connection between aerobics social adaptability and various dimensions The cultivation of student' s social adaptability. In this paper, the GMM algorithm is mainly used to distinguish the rest time when the action occurs, and the subsequent rest period is used as the basis for segmenting multiple events in the action sequence. Finally, the characteristics of the action coding mapping of each event are derived, and the support vector machine is used to complete the energy recognition process of the existence of a single energy. The experimental results of this paper show that the designed embedded image acquisition system has high integration and stability, the acquired image resolution is 640x480, and the wireless transmission rate is 5MbPs, which has wide application prospects.

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Section: A Aerobics Training Action Recognition Data Setmentioning

confidence: 99%