The live broadcast of the common league mainly adopts the design of streaming media data block by block. The secondary processing of streaming data is carried out on the server-side. It results in a higher delay during a live broadcast. The higher delay has numerous disadvantages during broadcasting for delay-sensitive applications. We have to solve the delay problems during a live broadcast. Therefore, in this paper, we optimize the synchronization of the tennis professional league live broadcast based on wireless network planning. In the proposed scheme, the 3D detection target model is first constructed, and the background is extracted from the moving video image. The extraction is performed using the interframe difference elimination algorithm, and the target motion trajectory is predicted. Then the phase difference characteristics of the image foreground trajectory are analyzed by the Hilbert transform to detect target missing points. By observing the vertex structure of the target missing point, the phase Fourier transform frame is constructed. Finally, the synchronization of the league live broadcast is encoded and decoded. Based on the timestamp, the synchronization optimization of the tennis professional league live broadcast is completed. The experimental results show that the synchronization optimization method has good synchronization, high resolution, short reaction time, and good detection effect.
With the development of science and technology, the carriers and communication platforms for knowledge accumulation have become more and more abundant in modern society. In modern society, as an interactive platform with high efficiency, real-time, and wide dissemination scope, the Internet has gradually become an important force for promoting economic development and social progress after more than 40 years of rapid development [
At present, the main ways to broadcast live events are not limited to live television, live text pictures, live Internet, and studio interviews categories. Among these live broadcasts, professional tennis league live broadcasts have the most intuitive and clear visual effects. The TV live broadcasts are equipped with very professional commentators, which provide an effective way for sports enthusiasts to understand the events and athletes fully. Hence, it is very popular with sports viewers. The reference [
The rest of the paper is organized as follows. In Section
This section discusses a target missing point detection model, a target missing point detection method, and a feature optimization method of target missing point.
Figure
Model of detecting missing point in tennis professional league live video.
The model for detecting missing points of the tennis professional league live video object extracts the background from the tennis player’s sports image. It predicts the trajectory, then carries on the real-time tracking, and deletes the background to leave behind the foreground containing the target (namely, tennis in motion). It also detects the situation of the missing point of the target. The process based on wireless network planning is generally divided into three stages: the preliminary preparation for the planned tennis professional league lives broadcast, the pre-planning for the tennis professional league live broadcast, and the detailed planning for the tennis professional league live broadcast [
Detection model workflow.
As shown in Figure
Let the coordinate point of the frame-block mapping matrix in the
where
The frame-block mapping matrix is the optimal solution for trajectory prediction of target missing points in live video of professional tennis league. Therefore, it is very important to solve the trajectory prediction parameters accurately. The proposed detection method of target missing points in live video of professional tennis league uses frame-block matching algorithm to solve the trajectory prediction parameters [
By calculating
After the foreground of the moving image is obtained from the detection model and denoised, the proposed target missing point detection method is used for live videos of professional tennis leagues. It performs the Hilbert transform on the foreground frame to obtain the target missing point signal [
where
where
Suppose the initial phase of the target missing point signal is
2.3.1. Introduction of Optimization Method of Loss Point in Tennis Professional League Live Video Broadcast When the vertex of the target missing point presents where where where When the vertex of the target missing point does not have local stability, the error of the optimized parameter
In this section, we elaborated on various aspects of the stabilization and synchronization of actions from video.
There are some jitter and noise in the live tennis professional league game video captured by the mobile high-altitude camera. Therefore, using the stable frame to stabilize the professional tennis league live video provides the foundation for follow-up synchronization optimization. Video stabilization includes the following steps: Feature detection and matching: the fast corner detection algorithm is used to find the key points in the live video frame of a professional tennis league. The fast corner detection algorithm is a time- and memory-efficient method for calculating stable video features [ Homography estimation: after feature matching, homography estimation is performed between the corresponding points of two frames. In homography transformation, eight parameters are coded to estimate the translation, rotation, zoom, tilt and view angle transformation at one point. The matrix where Parameter smoothing: The singleton transformation obtained in the above steps describes the camera motion after the first frame and can obtain a cumulative transformation relative to the first frame. Subsequently, the Savitzky–Golay smoothing filter was applied to smooth the motion parameters [ Savitzky–Golay filter is a data set, which is processed in a continuous 2 M 1 data point, and its fitting polynomial is as follows: The minimum mean squared error is Frame bending: Finally, the calculated motion parameters are bent into each frame to get a stable professional tennis league lives video.
Once a stable professional tennis tournament live video is obtained, the spherical features can be extracted from these frames. The following two features are selected here: Yellow plane intensity characteristics: Because the tennis ball’s color is yellow green, the yellow object on the yellow plane looks white. To this end, this color cue can be used to segment the sphere in the frame [ In this step, the three color planes are extracted from the image. A matrix representing the yellow intensity is created according to the following equation. A threshold is set to separate the sphere from the background. Figure where Phase quaternion Fourier transform (PQFT) characteristics: Each pixel of an image is represented as a quaternion, namely, RG color channel, BY color channel, intensity channel, and motion channel. The position of the salient region is obtained by using the phase spectrum of the Fourier transform. Figure
Different frames: (a) sample frames, (b) frames in the yellow plane, and (c) frames after the threshold are applied.
Frame-salient phase Fourier transform: (a) sample frame, (b) PQFT salient image of the frame, and (c) frame after the threshold is applied.
In this paper, the FFmpeg libavcodec, libavformat library, is mainly used to decode tennis professional league live video. First, the video source device collects tennis professional league live video information through the camera and sound pickup module, then encodes and compresses it through FFmpeg, and then packets it, that is, divides a sequential and continuous data stream into small segments called “packets.” After the video and the audio streams are packed, the head of the packet is added before each packet for distinguishing. PTS and DTS should be stamped in the packet header, where PTS is the display timestamp, and DTS is the decoding timestamp. And the data are transferred to the player via the network for playback. The decoding process is as follows: the media stream readout through TCP protocol in the network is separated and decoded by FFmpeg. Finally, the original video data YUV and audio data PCM are obtained. The video is finally played. The tennis professional league live video data collection and playback process is shown in Figure
Tennis professional league live video data collection and playback process.
In this paper, the H264 algorithm is used for images, and the AAC algorithm is used for audio. The data stream has to be read through the established TCP connection; the image and the audio data have to be separated from the video stream. It stores separated image and audio data in their respective buffer queues. Each connection starts its independent processing threads, reads the frame data from the queue, and decodes it. Then according to the time node, the image is sent to the graphics card, and the audio is sent to the sound card for display and playback. The DTS and PTS of audio and video frames can be obtained by calling image and audio decoding library functions. The synchronization algorithm of the tennis professional league live video designed in this paper mainly depends on DTS and PTS.
On the mobile phone side, the tennis professional league live video is decoded into the buffer zone. The synchronous control is carried out between the buffer zone and the control program of tennis professional league live video playing equipment. This paper uses a sampling rate of 44100 Hz. Because the audio sampling rate is fixed, but the video sampling rate is not fixed, so the audio sampling rate is the benchmark.
Mainly, audio, video, and audio synchronization is the use of fixed audio sampling rate characteristics. First, the basic reference clock of audio synchronization is obtained by using library functions. Call the function to decode the image, and the DTS will be stored in the corresponding variable during decoding. The DTS determines when the image is decoded and decodes the current average video frame rate based on the number of video frames in the audio time interval per second. Suppose there is an error, such as the video is earlier than the audio. In that case, the average frame rate is reduced, and if the video is later than the audio, the average frame rate is increased, thereby controlling the synchronization of live tennis professional league video by time-killing.
When we get the
where
However, in most cases, the frame rate is not fixed, and dynamic prediction is used to solve the frame rate. The start time of the next frame is predicted by estimating the start timestamps of the current frame and the previous frame. The frame rate is the reciprocal of the time difference, and the frame rate of the next frame is dynamically updated to the frame rate. The process of calculating the start time of the next frame is shown in the following equation:
where
In order to synchronize the real-time video and audio of the professional tennis league, first, set a refresh function to refresh the video frame and set a key point to refresh the next frame, that is, set the refresh interval Rgh. After the current frame is refreshed, it is necessary to continue to calculate the new refresh interval. The function get_clock () is used to get the playback time, that is, the time of reference clock. The function get_audio_time () is used to get the playback time of the current audio, that is, divide the sample number of the packet obtained by decoding the sampling rate to get the playback time current audio and add it to the reference clock to get the playback time of the current audio. At the same time, the PTS calculation of the live video frame of the professional tennis league is used. We can get the real-time (nonpredicted) start time of the current frame. Then get the delay arrival time of the current frame by subtracting the time twice. If the delay time is positive, it means that the current frame is early, that is, it refreshes the current frame in advance. If it is negative, it means that the current frame is early, and the next refresh delay value will increase. If it is late, the next refresh delay value will decrease. The following formula can be used to update the refresh delay of the refresh function:
where
Tennis is a worldwide sports event. Most of the tennis matches broadcast live in the professional league are foreign events. In order to verify the effect and feasibility of optimization of synchronicity of tennis professional league broadcast based on wireless network planning, experiments were conducted. In order to make the experimental data more reasonable, the subjects chosen are all inexperienced or inexperienced novice players. The completely inexperienced tennis players are divided into the second group, and the others are divided into the first group.
The main objective of this experiment is to analyze the effectiveness of the proposed optimization method based on wireless network planning, including the resolution ability, reaction time, and optimization measures. Because the definition of the effectiveness of optimization measures is vague, the experiment describes the effectiveness of optimization measures by analyzing the phase difference of track before and after optimization. The resolution of the target missing points is replaced by the resolution accuracy. The resolution and the reaction time also indirectly express the detection effect of the method.
The experiment was carried out three times. This method’s experimental methods, reference [
The commentator understands the English data shown in the referee’s voice and on TV during the match in the live broadcast. In the closing stage, the commentator translates the interview with the athlete during the prize presentation. Therefore, as an excellent tennis TV commentator in our country, we must have some foreign language translation ability. There are two modes of personnel composition of tennis TV commentary: two-person and one-person commentary modes. The Grand Slam final events broadcast live on CCTV channel CCTV-5 from 2012 to 2015 is as shown in Table
CCTV Grand Slam event commentators in recent four years.
Time/year | The Australian Open | The French Open | Wimbledon Championships | The US Open |
---|---|---|---|---|
2012 | Tong Kexin Xu Yang | Zhang Sheng Xu Yang | Tong Kexin Xu Yang | Tong Kexin |
2013 | Tong Kexin Xu Yang | Zhang Sheng | Tong Kexin Xu Yang | Zhang Sheng Xu Yang |
2014 | Tong Kexin Xu Yang | Tong Kexin | Zhang Sheng | Tong Kexin |
2015 | Xu Yang Zhang Sheng | Tong Kexin Xu Yang | Zhang Sheng | Zhang Sheng |
As can be seen from Table
Based on the statistics and analysis, SPSS 22.0 software results from tennis players’ behavior data analysis are described in Table
Analysis of tennis player behavior data under different methods.
Method name | Analytical project | Group one | Group two |
---|---|---|---|
Resolution accuracy (%) | 91.42 | 88.64 | |
Reaction time (ms) | 316.03 | 363.25 | |
Resolution accuracy (%) | 82.51 | 80.19 | |
Reaction time (ms) | 373.28 | 296.74 | |
Resolution accuracy (%) | 70.58 | 66.37 | |
Reaction time (ms) | 413.21 | 446.59 |
The data in Table
Because there is no significant difference between the two groups of tennis players, only the first group of tennis players is tested to analyze the effectiveness of optimization measures. Figure
Comparison of the phase difference of trajectory: (a) before optimization and (b) after optimization.
We know that the phase difference of the track reflected by tennis players has been stabilized gradually by analyzing the results. The area of the drop point is smaller that proves that the proposed method shows the best optimization effect and the most effective.
In this section, we discuss the conclusions and the prospects of the study.
In the live broadcast of the tennis league based on wireless network planning, the synchronization results are better than that of existing methods. The accuracy rate of optimized resolution of live broadcast synchronization of professional tennis leagues is higher. The reaction time is smaller, and the detection effect is fast and efficient. The optimization effect of live broadcast synchronization of professional tennis leagues based on wireless network planning is the best and most effective than that of the existing methods.
Although some better results have been achieved in this paper, there is room for improvements due to the complexity of the live broadcast system and some other factors. In the field of mobile live streaming, it is necessary to have a minimum delay in the live streaming systems by dynamically reducing the frame rate and image quality of real-time streaming media in wireless networks. The functions of the live server of the professional tennis league still need to be enriched. It is possible by optimizing the slicing function of live audio and video data, the live server’s distributed deployment, and the load balance among clusters. In today’s increasingly severe network and information security, it is necessary to encrypt professional tennis league lives broadcast data. Simultaneously, multiple security protection and loophole investigations shall be carried out for the system.
The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
The dissertation is a part of the Key Program of 2020 Hubei Educational Science Planning: An Empirical Study on the Teaching Methods and the Quality Enhancement of Tennis Teaching in Universities in Hubei (no. 2020GA079).
The authors declare that they have no conflicts of interest.