27-09-2016, 11:10 AM
1456310588-HawkEye2.DOCX (Size: 493.71 KB / Downloads: 4)
ABSTRACT:
Many sports have become very reliant on monitoring systems such as Hawk-Eye in Tennis; this paper has looked into just how accurate the technology is, and assessed the effectiveness of the technological approaches used to implement the system. The level of accuracy is vital for sport monitoring systems, in a number of sports such as Line Calling Decisions in Tennis. Even a small margin of error can affect the decision of whether the ball is called in or out. As many high profile sporting industries have placed a great deal of dependence upon this technology, if it were to prove inaccurate, the sporting world would incur devastating consequences. As governing bodies would then be under a large amount scrutiny, from all over the world. Players, coaches and even spectators would all start to question the decisions made using this technology made in past. And as these were designed to rule out human error in such cases as line calling, any major failings found in the technology would render them useless.
with various applications where one sees this technology being put to use.
HAWK-EYE: A GENERAL OVERVIEW
Cricket is a ball game played within a predetermined area. A system comprising of video cameras mounted at specific angles can be used to take pictures. These pictures are then used to locate the position of the ball. The images are then put together and superimposed on a predetermined model to form a complete visualization of the trajectory of the ball. The model includes, in this case, the pitch, the field, the batsmen and fielders etc. For this to be possible, we need to sample images at a very high rate and thus need efficient algorithms which can process data in real time. Such technologies are widely used today in various sports such as Tennis, Billiards which also fall in the category of ball games played within a restricted area. Our discussion will mostly contain applications which specific to the game of cricket, however in some cases, we will mention how similar techniques are applied in other games.
There are various issues which crop up when one tries to design and implement such a system. In the game of cricket, the general issues are:
1. The distance at which the cameras see the pitch and the ball are dependent on the dimensions of each ground and can vary greatly.
2. Just the individual images don‟t help too much; for the system to be of practical use, one must ensure that it can track the 3D trajectory of the ball with high precision. In order to get this accuracy, the field of view of each camera should be restricted to a small region – this means one needs more cameras to get the coverage of the entire field.
3. Fielders and spectators might obstruct the camera‟s view of the ball and the ball might get „lost‟ in its flight in one or more of the cameras. The system should be robust enough to handle this, possibly by providing some redundancy.
4. The ball might get confused with other similar objects – for instance, with flying birds or the shadow of the ball itself. The image processing techniques used need to take care of these issues. Luckily, there are techniques which are easy to implement and are well known to the Image Processing community on the whole, to take care of these.
5. To help in judging LBW calls, the system needs to be made aware of the style of the batsman – whether he is right or left handed. This is because the rules of LBW are dependent on the position of the stumps and are not symmetrical about the middle stump. Thus, the system needs to detect whether a particular ball has pitched outside the leg stump of a batsman or not.
6. To determine the points at which the ball makes contact with the pitch, the batsmen or other objects is very hard. This is because we don‟t really know these spots beforehand and the model and the real pictures taken by cameras need to be merged to give such a view.
Preparation before starting to process:
Additional features might be loaded into the system to enable it to process the data in a more reliable and useful manner. These might include a statistical generator, which is used to produce statistics based on the data collected. These are the statistics which we see on television during and after the match for analysis. Such statistics can also be used by teams and players to study their game and devise strategies against their opponents. Indeed, the raw data about the paths of the ball might be too much for any human to digest and such statistics turn out to be easier to handle and understand. The statistics generator might also aid in storing data such as the average velocity of the ball. This data is crucial as it can help the ball detection algorithm to predict the rough location of the ball in an image given the position in the previous image. Such considerations are useful to reduce the computations involved in the processing of the data collected from the video cameras. Once such additional machinery is setup correctly, we are all set to start collecting data and start processing it to churn out