AUTOMIZATION OF UMPIRING IN CRICKET USING FUZZY LOGIC

  Fuzzy logic has been introduced to deal with vague, imprecise and uncertain problems. A fuzzy logic controller can be regarded as an expert system that is able to process qualitative variables and to infer crisp values out of uncertainty. Hence, fuzzy logic can find applications in many aspects of real life, where there is lack of information, there is uncertainty. A good example of such an application is in AUTOMIZATION OF UMPIRING IN CRICKET

RULE BASE

A Fuzzy Decision Making System

Cricket now- a -days has gone beyond the scope of a ‘game' to be a inherent part of our senses. While millions of money is invested in this sport there exist still some discrepancies that hit heavily on the reverence to the game. Humans are always prone to errors, which hold well even with the umpires. Thus automization of the decisions will help to improve the essence of the game and ensure an even more cheerful and judicious entertainment. This paper puts forward a proposal, which aims at achieving the following objectives

The main objectives we propose to solve are:

•  Unravel the contradiction between a boundary and a six.

•  Identify an LBW.

•  Identify a catch.

•  The tool we have used is “Fuzzy Logic” as it is a good decision maker.

•  Neural networks can be trained using real time data which makes it highly efficient in operation.

WHY USE “FUZZY LOGIC” AS A TOOL?

  Fuzzy Logic methodology, a branch of Artificial Intelligence is basically characterized by three traits.

•  First, it does not consider whether something is true or false, but rather how true the statement is.

•  Second, because it is similar to human reasoning, its implementation tends to be based on natural language.

•  The third trait is that it is flexible and can model a complex, non-linear system by using imprecise information.

The mode that has just been described provides an immediate output which makes the fuzzy as the choicest tool for our problem.

FUZZIFICATION

  •  A membership function acts on input variables usually from sensor data, in what is known as a fuzzifier.

  •  The fuzzifier output is referred to as a fuzzy-data value, which is the input to the rule evaluator, which compares the fuzzy-data value to the value established for each rule.

  •  If one rule seems to be dominant explanation for the fuzzy-data value it is considered to have ‘won'.

  •  This news can be de-fuzzified for our real values.

 
  FIELD SETUP

  •  We install cameras at appropriate places, which are capable of providing the fuzzy system with the input values angle, height and distance. These data are then manipulated to determine the outcome of the delivery of a ball.

  •  The cameras may be installed at the top of the stadium to provide a panoramic view, which eases the task of measuring the height.

•  The cameras may also be installed around the ground to measure other parameters like angle.

  •  All the cameras will always be tracking the ball, so that we have a lot of angles to look at the ball.

PARAMETERS INVOLVED

Three input parameters are involved viz height ,distance and angle.

 •  Height:

This parameter is used to distinguish a boundary from a six. Also it is used to identify a catch.

•  Distance :

This parameter is used to determine if the ball has crossed the boundary line.

•  Angle:

This parameter is used to identify an LBW.

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