一个可定制的AI甲板球场报告和自动III裁判决定审查系统DRS

P. Ramya, C.P. Gowtham, S. K. Kumar, T. P. Silpica, P. Renugadevi
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摘要

如今,由于现代板球中某些有争议的方面,给予公平裁决是一项相当具有挑战性的任务。因此,为了避免做出错误的决定,我们开发了一个基于人工智能的自动化解决方案。该项目主要研究一种技术,可以帮助主裁判员和第三裁判员在三柱前(LBW)对击球手是否出局做出关键判断,并最大限度地减少球员等待时间,直到第三裁判员通过球的轨迹做出正确的决定。我们的AI-DRS的主要目的是消除裁判员的判罚,裁判员的判罚在给予第三裁判判罚的过程中起着至关重要的作用,因为任何一种情况显示裁判员的判罚都将与现场裁判员的判罚保持一致,无论该判罚是否出局。球场报告和全面的板球规则也包括为了比赛。球场报告将检查几个关键的小门特征,如土壤类型,裂缝,草覆盖的数量和湿度等。我们使用无人机捕捉比赛日球场的视频。为确定现场裂缝,采用精细边缘检测和土壤水分传感器测定土壤含水量。这些信息有助于板球队在赢得投掷后决定是击球还是上场,并有助于选择当天在该球场上赢得比赛的最强11名球员。利用支持向量机(SVM)和梯度直方图(HOG)对目标进行分类和识别。为了监测和预测球的速度,应用了线性回归和二次回归。最后,使用Tkinter进行GUI开发,使用imutils和OpenCV作为实现工具。由于罕见的wicket调用的争议,边界和罚款,我们把一个声音公认的AI系统,给粉丝们很容易理解为什么这个决定是由裁判和裁判的某个时候发现很难记住一些规则在板球很少使用它也会给协助场上裁判给一个非常明确的知道为什么他决定,一秒钟内作出取代场上裁判可以很容易地通过语音识别使用Alan-AI访问的法律。语音识别web应用程序是使用react-js开发的。
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A Customisable AI Deck for Pitch Reports and Automated III Umpire Decision Review System DRS
Nowadays giving fair verdict is a quite challenging task because of certain contentious aspects in modern cricket. So, in order to avoid making wrong decisions, we develop an automated AI-based solution. This project focus on a technology that helps both the main umpire and third umpire to makes critical determination for Leg Before the Wicket (LBW) regarding whether the batsman is out or not-out and also minimizes the waiting time for players until the third umpire go through the trajectory of the ball to make a correct decision. The main purpose of our AI-DRS is to remove the umpires call which plays a vital role in giving third umpires decision because whether any one of the cases shows umpires call the decision will be stick with on-field umpires call whether it may be out or not-out. The pitch report and comprehensive cricket laws are also included for the sake of the game. The pitch report will be examined with several key wicket characteristics, such as kind of soil, cracks, amount of grass cover, and wetness, etc. using drone we capture the video of the match day pitch. To determine the field crack, canny edge detection is performed and soil moisture sensor is used to determine the moisture content of the soil. This information help cricket team to make a decision about whether to bat or field after winning the toss and helps to choose the strongest 11 players through which can win the match on that pitch on that day. Utilizing support vector machine (SVM) and histograms of gradients (HOG), objects are classified and recognized. In order to monitor and forecast the velocity of the ball, linear regression and quadratic regression are applied. Finally, Tkinter is used for GUI development, imutils and OpenCV are used as implementation tools. Due to the controversy of rare wicket calls, boundary and penalty runs, we bring a voice recognized AI system which gave fans to easily understand why this decision is made by the umpire and sometime umpires found difficulty to remember some rules which is rarely used in cricket it will also give assist to on-field umpires to give a very clear idea why he made the decision, the on-field umpires can easily access the laws through voice recognition which use Alan-AI. The Voice recognition web app was developed using react-js.
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