An adaptive recognition method for take-off action images of back-style high jump based on feature extraction

Zhai, Lijie; Duan, Haisheng; Chen, Donghui

doi:10.1016/j.future.2021.07.032

Cited by 2 publications

(9 citation statements)

References 10 publications

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“…When the number of experiments reaches 60, the true positive rate of different algorithms reaches the maximum value. The true positive rate of algorithms in the paper is 0.98, which is 0.23, 0.26, 0.24, 0.32, 0.29 higher than the algorithms in BRMFSJ [10], POSL [1], AHDM [16], ARMHJ [22] and HARM [13], respectively, It shows that compared with the experimental comparison algorithm, the true positive rate of the algorithm in this paper is higher, which proves that the recognition result of this algorithm is more accurate.…”

Section: Resultsmentioning

confidence: 80%

“…After the implementation of the confidence fusion scheme, the final jumping action recognition result from figure skating videos is obtained. [10], POSL [1], AHDM [16], ARMHJ [22] and HARM [13] are used for jumping action recognition, and then the true positive rate is calculated according to the action recognition results.…”

Section: Data Setmentioning

confidence: 99%

“…The features are input to improve the deep reinforcement learning network model, and the jump action recognition results are obtained. At the same time, the algorithms in BRMFSJ [10], POSL [1], AHDM [16], ARMHJ [22] and HARM [13] are applied to perform action recognition on UCF 101 dataset. According to the recognition accuracy and recall of different recognition algorithms, the PR curve image is drawn, and then the area under the curve is calculated by 11 point interpolation method to obtain the AP value.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…However, the recognition accuracy of this algorithm is low. Zhai et al [22] used wavelet coefficient transform to get the noise threshold of moving video image. Then the wavelet coefficient matrix was constructed based on decomposing the image multi-resolution, and the image noise was removed according to the threshold.…”

Section: Introductionmentioning

confidence: 99%

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Jumping Action Recognition for Figure Skating Video in IoT Using Improved Deep Reinforcement Learning

Zhou²

2023

ITC

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Figure skating video jumping action is a complex combination action, which is difficult to recognize, and the recognition of jumping action can correct athletes’ technical errors, which is of great significance to improve athletes’ performance. Due to the recognition effect of figure skating video jumping action recognition algorithm is poor, we propose a figure skating video jumping action recognition algorithm using improved deep reinforcement learning in Internet of things (IoT). First, IoT technology is used to collect the figure skating video, the figure skating video target is detected, the human bone point features through the feature extraction network is obtained, and centralized processing is performed to complete the optimization of the extraction results. Second, the shallow STGCN network is improved to the DSTG dense connection network structure, based on which an improved deep reinforcement learning action recognition model is constructed, and the actionrecognition results are output through the deep network structure. Finally, a confidence fusion scheme is established to determine the final jumping action recognition result through the confidence is established. The results show that this paper effectively improves the accuracy of figure skating video jumping action recognition results, and the recognition quality is higher. It can be widely used in the field of figure skating action recognition, to improve the training effect of athletes.

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Section: Resultsmentioning

confidence: 80%

Section: Data Setmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Jumping Action Recognition for Figure Skating Video in IoT Using Improved Deep Reinforcement Learning

Zhou²

2023

ITC

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“…The finger vein recognition process usually consists of three stages: (1) preprocessing, including extracting a region of interest and image enhancement (Qu et al,2022), (2) extracting features (Zhai et al,2022), and (3) matching and identification, feature vectors are matched between test samples and training samples and then features are efficiently classified and recognized. The most important step is feature extraction, which has a great effect on recognition performance.…”

Section: Introductionmentioning

confidence: 99%

Finger Vein Recognition Based on a Histogram of Competitive Gabor Directional Binary Statistics

Wang

et al. 2023

Journal of Database Management

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Traditional methods of extracting finger vein texture changes and orientation features are susceptible to illumination, translation, noise, and rotation, and the process has difficulty directly extracting structural features through the original image. In this paper, the histogram of competitive Gabor directional binary statistics (HCGDBS) is proposed to extract discriminative structural features. First, the index of the largest filter value is obtained based on the multidirectional Gabor filter as the dominant direction, thereby obtaining the rotation-invariance feature. Second, according to the filter response size of each pixel in different directions, the order difference relationship between the adjacent three directions is compared, and a highly discriminative competitive Gabor direction binary pattern (CGDBP) is constructed. Finally, the CGDBP features are extracted in blocks, and the HCGDBS is constructed to overcome image translation. Experimental results show that it improves the recognition performance and overcomes illumination, translation, noise, and rotation.

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An adaptive recognition method for take-off action images of back-style high jump based on feature extraction

Cited by 2 publications

References 10 publications

Jumping Action Recognition for Figure Skating Video in IoT Using Improved Deep Reinforcement Learning

Jumping Action Recognition for Figure Skating Video in IoT Using Improved Deep Reinforcement Learning

Finger Vein Recognition Based on a Histogram of Competitive Gabor Directional Binary Statistics

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