Please select the most similar patent number from A, B, C and D. Which number is?
Disclosed are an assisted-training system and method for billiards. The system comprises a camera, a client end and a server, wherein a user can select a subject for training according to requirements, and then the trajectory of motion for each ball is compared with an evaluation standard corresponding to the subject for billiards training and analysed to obtain a training result. The system accurately judges whether a ball strike has been successful at the present time by means of billiards strike data, aiding in performing a scientific classification of the level of a player and an accurate analysis of the ball strike of the player, and providing detailed suggestions on rectification and improving the effects of the training.
A: A projection type billiard system gesture/billiard-rod control system and an implement method thereof. The system comprises an image capture device (7), a projection device (6) and a computer (3). The signal output end of the image capture device (7) is connected to the input end of the computer (3). The input end of the projection device (6) is connected to the output end of the computer (3) and controlled by the computer (3). A hollow billiard lighting lamp (4) and an infrared light supplementary lamp (5) are further provided. The method includes that the computer (3) controls the projection device (6) to project an operation interface comprising various function buttons in a system program on a table surface of a billiard table (8). The computer (3) controls the image capture device (7) to capture images in the scope of the table surface of the billiard table (8) in real time. Background comparison analysis and previous-frame comparison analysis are carried out on current images, and a changed image area in the images is further obtained. The part, close to the billiard table (8), in the changed area is set to be the initial end of the changed area, and the other end is the tail end of the changed area. When the residence time of the tail end in a certain function button area scope of the operation interface is longer than a preset time, it is considered that the button is clicked and selected by a user, and the computer (3) operates the corresponding system function.
B: The present disclosure relates to a training method based on multi-dimensional movement ability recognition. The technical solution comprises: constructing an action library consisting of multiple pieces of standard action data; collecting movement image information of a training object, and correspondingly calibrating key nodes and intermediate nodes in extracted key frames; on the basis of movement transformation states of the key nodes in the key frames, performing simulation and calculating movement vectors of the key frames, and, referring to intermediate nodes, performing analysis and calculation on the movement vectors to obtain ability values of the flexibility, agility, and strength of the training object for practicing corresponding standard movements; determining whether the training object is qualified according to the ability values, and according to unqualified movement abilities, generating a training plan after selecting a corresponding video stream from the action library. By means of the multi-dimensional movement ability recognition and analysis on the flexibility, agility, and strength of the training object, targeted training can be carried out accurately according to the flaws of the training object, the error in training purpose is small, and the training effects are significantly improved.
C: A method and system for competitive state assessment of athletes. The method comprises: controlling an acquisition module (61) in a wearable apparatus to acquire physiological data of an athlete (S101); calculating competitive state parameters of the athlete, and assessing whether the competitive state of the athlete satisfies training requirements (S102); if the competitive state of the athlete satisfies the training requirements, outputting a training permission prompt (S103); and if the competitive state of the athlete does not satisfy the training requirements, outputting a training prohibition prompt (S104). After acquiring the physiological data of the athlete, the wearable apparatus calculates the competitive state parameters of the athlete, and assesses the competitive state of the athlete on the basis of the competitive state parameters, so that more accurate and reliable competitive state assessment for athletes can be achieved. By automatically determining, after obtaining a competitive state of an athlete, whether the athlete satisfies training and competition requirements according to the competitive state, and outputting a corresponding prompt, the athlete can intuitively know whether own current competitive state is suitable for training and competitions.
D: The present disclosure relates to a training method based on multi-dimensional movement ability recognition. The technical solution comprises: constructing an action library consisting of multiple pieces of standard action data; collecting movement image information of a training object, and correspondingly calibrating key nodes and intermediate nodes in extracted key frames; on the basis of movement transformation states of the key nodes in the key frames, performing simulation and calculating movement vectors of the key frames, and, referring to intermediate nodes, performing analysis and calculation on the movement vectors to obtain ability values of the flexibility, agility, and strength of the training object for practicing corresponding standard movements; determining whether the training object is qualified according to the ability values, and according to unqualified movement abilities, generating a training plan after selecting a corresponding video stream from the action library. By means of the multi-dimensional movement ability recognition and analysis on the flexibility, agility, and strength of the training object, targeted training can be carried out accurately according to the flaws of the training object, the error in training purpose is small, and the training effects are significantly improved.
Answer: A