CZ-Base: A Database for Hand Gesture Recognition in Chinese Zither Intelligence Education

Zhao, Wenting; Wang, Shigang; Wang, Xuejun; Zhao, Yan; Li, Tianshu; Lin, Jiehua; Wei, Jian

doi:10.1007/978-981-16-1194-0_25

Cited by 4 publications

(2 citation statements)

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“…While sectioning the pictures of the participants in the movie, the estimate indices "time" and "difference in pixels" are used. The computation of the image precision is shown in Formula (10).…”

Section: Segmentation Of Playersmentioning

confidence: 99%

“…These methods, nevertheless, call for the placement of numerous cameras all throughout the playing area, which is significantly more costly than single perspective collection systems and seems challenging to implement in many settings. Inertial device sign language [9] and picture gestures [10] are the two primary kinds of basketball gesture recognition techniques. The inertial camera pose cohesion necessitates the use of the sensor by the athlete, who then sends the data obtained to the data processing terminal for action recognition analysis.…”

Section: Introductionmentioning

confidence: 99%

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Basketball Tactics Analysis Based on Improved Openpose Algorithm and Its Application

2024

jes

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The use of information technology has currently resulted in a change in people's production and lifestyles as well as a catalyst for the sports industry's evolution. Currently, there is a growing use of digitization in the sports industry's basketball realm. Using 2D posture estimation, a novel lightweight deep learning architecture (LDLA) is built to accomplish the automatic analysis of basketball game footage. We offer a real-time method to identify numerous people's 2D poses in a video. Additionally, it examines how 2D pose estimation might be used to analyse basketball shooting videos. First, group and global motion features are extracted to represent semantic events. A full basketball game video is broken down into three stages: clip-based segmentation, classification of semantics occurrences utilizing audio and video, and temporal sequence characteristics employing GRU CNN. Video is first processed to create some basic video categorization and segmentation using the visual, movement, and auditory data. Further use of domain knowledge is made to find important events in the basketball video. Shot and image threshold recognition algorithms for videos have used both optical and kinematic prediction information; scene classification comes next. The placements of probable semantic events, such as "fouling" and "shooting at the basket," are then discovered by comparing the multidimensional data with supplementary domain expertise. Experimental results demonstrate the proposed LDLA method achieves 99.6% of accuracy, 93.2% of precision, 93.5% of recall and 89.5% of F1-score.

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Section: Segmentation Of Playersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%