4.1 Constructing a basketball shot recognition model
There are many complicated human structure movements involved in basketball shooting. Before designing a basketball shooting technique recognition model, it is necessary to classify the basic basketball shooting postures. In order to effectively determine the basketball movement according to the physical condition of the basketball player, it is first divided into sports state and static state. The state of the game corresponds to the state of the players when they complete various basketball actions. At present, the limbs of athletes are in motion; statistics refer to the situation where the limbs of athletes are completely still. The focus of basketball gesture recognition is to recognize various sports gestures. In order to effectively recognize different sports postures in basketball, the sports postures are gradually divided into two stages. First, according to whether the motion state is periodic, the posture of the human body is divided into two categories: continuous action and instantaneous action. The second step is to divide the body’s posture into seven postures of walking, running, dribbling, jumping, shooting, passing, and catching the upper or lower limbs according to whether the state of the action is exercise. The basketball position recognition model automatically recognizes the seven sports positions of basketball players.
4.2 Sensor signal collection
Many sensor devices including an accelerometer, a gyroscope, an angular velocity meter, a pressure sensor or the like, in the data collection phase collect body posture information and perform different actions. The basic method of human body movement posture recognition is to install sensors on the key parts of the human body to detect the limb movement information of the human body. In basketball shooting action recognition, the movement information of the legs and arms of the human body is mainly collected. The sensor node formed by the combination of multiple sensor devices can convert the action information during the completion of the action into electrical signals for uploading and fulfill the requirements of subsequent logic operations, data storage, and communication. According to actual application requirements, it is difficult for a single sensor module to meet the work requirements. The information required in human posture recognition is complex and diverse, including physical and physiological information such as acceleration, angular velocity, or heart rate. The internal analysis and processing of the node needs to be completed, so the design of the node needs to include multiple sensor modules, which can be used in conjunction to complete the work requirements of the system. Generally, a sensor node includes four modules, which are mainly composed of four parts: processor module, power module, sensor module, and communication module. The processor module controls the normal operation of each functional module of the sensor node and performs the related processing of each signal; the sensor module realizes the function of detecting the movement information of the object, and realizes the transformation of the movement information to the electrical signal; the communication module is responsible for signal transmission, n nodes transmit wireless data to other devices; the power supply provides the energy for the normal operation of the entire sensor. At present, mobile devices such as mobile phones have also begun to integrate various sensor modules, which have the function of wireless communication. They will replace sensor nodes worn on key parts of the human body for signal collection. Compared with sensor nodes, mobile devices are worn at different locations. Fixed, this will have an impact on the recognition result of the system. When the sensor detects motion information, the device can be placed in a fixed position to avoid this impact.
4.3 Shot recognition
The essence of the basketball gesture recognition stage is to construct a classification model process that meets the basketball action data division. For each specific basketball action, after data collection, data preprocessing, data division, and feature extraction, a description of the specified basketball action can be obtained. The attribute set is the feature vector set. These feature vector sets are abstract data sets of basketball actions, and their corresponding classifications can be obtained through calculations in the classifier model. The attributes contained in the feature vector are complex. In order to eliminate irrelevant and redundant attribute values in the feature vector, it is necessary to perform feature selection on the feature vector. In the attribute selection, the first priority search algorithm and principal component analysis method are used. The feature selection realizes the dimensionality reduction of the feature vector, reduces the complexity of the classification calculation process, and improves the work efficiency of the system. In this experiment, sensor nodes are respectively fixed on the lower leg and forearm of the subject to detect the movement behavior information of different limbs. According to the different placement positions of the nodes, the data set of each movement is divided into upper limb movement data set and lower limb movement data set. In the action data set, classifiers are constructed for different sample sets to realize the specific division of the actions of the upper and lower limbs. The combination of the results of the upper and lower limbs can obtain the basketball movement posture of the current subject.
In this paper, support vector machine model (SVM) is used to identify basketball shooting techniques. When (SVM) solves two types of classification problems, it will look for an h-dimensional hyperplane in the h − 1-dimensional sample feature space as the segmentation plane for the two types of samples. Usually, this plane is called a linear classifier. When the samples can be distinguished correctly, they are said to be linearly separable. When it is necessary to deal with the case of linear inseparability, SVM will map sample points to higher-dimensional or even infinite-dimensional space. At this time, this mapping is nonlinear, so sample points will become linearly separable in high-dimensional space. In this case, using the k(x, y) function that satisfies the Mercer condition as the inner product operation of the two sample features is equivalent to mapping the sample from the original feature space to a new feature space. Suppose the sample feature is xi, the sample category label is yi, and the Lagrangian coefficient is ai, bcan be obtained by any support vector, then the corresponding optimal classification function is defined as:
$$ {f}^{\ast }(x)=\operatorname{sgn}\left(\sum \limits_{i=1}^N{a}_i{y}_i\bullet k\left({x}_i,x\right)+b\right) $$
(3)
This article also tries to use the Gaussian mixture model to eliminate interference from the background image of basketball shooting action recognition. In this model, assume that the pixel value of the recognized video at a certain moment t is Yt, and k is the Gaussian distribution number (generally 3 5), ϖi is the i-th Gaussian distribution weight, μi, t and σi, t represent the mean and variance, respectively, g is the Gaussian distribution function, then the random probability corresponding to Yt is:
$$ P\left({Y}_t=\sum \limits_{i=1}^k{\varpi}_{i,t}g\left({\mathrm{Y}}_t,{\mu}_{i,t},{\sigma}_{i,t}\right)\right) $$
(4)