Non-rigid registration of point clouds using landmarks and stochastic neighbor embedding

Maharjan, Amar; Yuan, Xiaohui; Lü, Qiang; Fan, Yuqi; Chen, Tian

doi:10.1117/1.jei.30.3.031202

Cited by 3 publications

(1 citation statement)

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“…Top-down approaches usually use advanced human detectors to first detect all people from the image and then scale the image block containing a single person to a fixed size to send to a single person pose estimator for prediction. Bottom-up approaches, on the other hand, do not rely on human detectors and directly infer the location information of all human key points in the image and group the key points to obtain the pose of all people [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Estimation Method of Human Moving Target Pose Based on Adaptive Attention Mechanism

Ding¹,

Zhao

2022

Wireless Communications and Mobile Computing

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In daily physical education, posture performance is an important basis for making excellent results. This paper explores an intelligent method to estimate the target pose based on adaptive attention mechanism. First, the regional attention is iteratively generated from a global level to a local level based on the attention mechanism. Human decision-making patterns are imitated to evaluate the effectiveness of regional attention in real time. The level of attention mechanism is adaptively adjusted and focused layer by layer to achieve precise target detection and tracking. Second, with the target frame obtained from each frame, the pose estimation algorithm finds the key points of human body, enabling the human body pose optimization strategy to solve the crossover problem of the key points. Results of experiments on sports video images show that the proposed method has a higher accuracy in pose estimation than other algorithms and can help sportsmen adjust their training methods scientifically.

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Section: Introductionmentioning

confidence: 99%