A Posture Recognition System for Rat Cyborg Automated Navigation

Zhang, Zhenchuan; Yang, Yingchun; Wu, Zhaohui; Ma, Qian

doi:10.1049/cje.2018.04.003

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…The region is segmented in the established coordinate system, and the real-time changes of contour feature extraction results are tracked. Taking the divided area of image region as the research object, the extraction result of area feature is obtained through accumulation calculation (Zhenchuan et al, 2018).…”

Section: Improved Motion Gesture Recognition Under Deep Neural Networkmentioning

confidence: 99%

An improved deep neural network method for athletes human motion posture recognition

Dong¹,

Wang²

2023

IJICT

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Aiming at improving the accuracy of motion gesture recognition and reducing the time-consuming recognition, this paper proposes an athlete's body motion gesture recognition method based on improved deep neural network. According to the movement characteristics and basic structural characteristics of the human body, the movement posture recognition standard is designed and set in the model. Collect athletes' human motion images, and perform motion image extraction, residual compensation, filtering, normalisation, and morphological processing. On this basis, the features of human motion images are extracted and fused. The recognition classifier is constructed based on the improved deep neural network, the fused feature vector is input into the classifier, and the recognition result is output. The results of the comparative experiment show that the proposed method has a high recognition rate and short overhead time only at 1.2 s.

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Section: Improved Motion Gesture Recognition Under Deep Neural Networkmentioning

confidence: 99%

An improved deep neural network method for athletes human motion posture recognition

Dong¹,

Wang²

2023

IJICT

View full text Add to dashboard Cite

show abstract

“…To achieve spatial motion alignment, Seulki et al used the rotational offset between the bodies (extracted from the 3D position of the shoulder and torso) [10]. The Moravec angle operator, which can simultaneously detect the points where the moving object changes greatly in the local space-time dimension [11], has been used to model and refine the 3Dharris operator proposed by Zhang et al Yan et al divided the human body on the back into five body parts and used a contrast mining algorithm to detect various postures of the body parts in the spatial domain, which he then collected to create a data dictionary [12]. Al-Qaness et al depicted motion as a continuous and differentiable function of changing body joint position with time and defined a window around the current time step in which the quadratic Taylor transform can be used to transform it locally [13].…”

Section: Related Workmentioning

confidence: 99%

Stage Performance Characteristics of Minority Dance Based on Human Motion Recognition

Shi

2022

Mobile Information Systems

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Due to the different development history of different nationalities, there is a big gap between traditional cultures. Especially, as an important embodiment and component of traditional culture, ethnic minority dance has unique characteristics. With the development of machine vision technology, human motion recognition has gradually become a hot research direction, but its application research in the field of dance motion recognition is still in its infancy. In this paper, the characteristics of stage performances of ethnic minority dances in the field of intelligent auxiliary training based on human motion recognition technology are introduced. The human body regions in the frame are obtained by using human posture, and 3D_SIFT and optical flow features are extracted from each region. Then, we use the extracted key frames and DTW (dynamic time warping) algorithm to realize the motion recognition of the motion capture data and carry out simulation experiments. The test results show that the algorithm can identify the minority dance video database, effectively identify the dance movements, and realize the movement correction of dancers.

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“…Starting from the field of surveillance and security, traditional surveillance technology is widely used in the military field, such as customs defense and borders. e detection and tracking technology based on deep reinforcement learning can be used to assist manual completion of designated tasks [6,7].…”

Section: Introductionmentioning

confidence: 99%

Human Motion Posture Detection Algorithm Using Deep Reinforcement Learning

Li-min

Han

2021

Mobile Information Systems

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To address problems of serious loss of details and low detection definition in the traditional human motion posture detection algorithm, a human motion posture detection algorithm using deep reinforcement learning is proposed. Firstly, the perception ability of deep learning is used to match human motion feature points to obtain human motion posture features. Secondly, normalize the human motion image, take the color histogram distribution of human motion posture as the antigen, search the region close to the motion posture in the image, and take its candidate region as the antibody. By calculating the affinity between the antigen and the antibody, the feature extraction of human motion posture is realized. Finally, using the training characteristics of deep learning network and reinforcement learning network, the change information of human motion posture is obtained, and the design of human motion posture detection algorithm is realized. The results show that when the image resolution is 384 × 256 px, the motion pose contour detection accuracy of this algorithm is 87%. When the image size is 30 MB, the recognition time of this method is only 0.8 s. When the number of iterations is 500, the capture rate of human motion posture details can reach 98.5%. This shows that the proposed algorithm can improve the definition of human motion posture contour, improve the posture detailed capture rate, reduce the loss of detail, and have better effect and performance.

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A Posture Recognition System for Rat Cyborg Automated Navigation

Cited by 6 publications

References 17 publications

An improved deep neural network method for athletes human motion posture recognition

An improved deep neural network method for athletes human motion posture recognition

Stage Performance Characteristics of Minority Dance Based on Human Motion Recognition

Human Motion Posture Detection Algorithm Using Deep Reinforcement Learning

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A Posture Recognition System for Rat Cyborg Automated Navigation

Cited by 6 publications

References 17 publications

An improved deep neural network method for athletes human motion posture recognition

An improved deep neural network method for athletes human motion posture recognition

Stage Performance Characteristics of Minority Dance Based on Human Motion Recognition

Human Motion Posture Detection Algorithm Using Deep Reinforcement Learning

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Resources

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An improved deep neural network method for athletes human motion posture recognition

An improved deep neural network method for athletes human motion posture recognition