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Vehicle Infotainment System

The object detection and tracking in particular human hands, has been widely used over the computer vision communication, due to which the hands are the most important  for communication which is implemented in human machine. Taking the information from the hand’s movement  is a very important activity as it may provide different information related to the vehicle like changing of the music, or driver’s attention, etc. In most  of the days, driver’s distraction has lead to various accidents like, total eyes off the road time and maximum glance duration. Performing various activities has lead to many accidents, therefore instead of driver’s attention put towards other activities while driving, he can just use his hand. The design of this system with an intuitive, robust, human machine interface based on the hand gestures which can be recognised by the machine. It relay’s on the raspberry pi camera which recognises the hand gestures and systematically assigned features for those actions.

    The analysis of behaviour of the driver’s fatigue is much most important and can be retrieved from the hand recognition while driving. The In Vehicle Infotainment system can be used with the standard two way communication with the voice commands. It includes rear seat capability which allows passengers to control the visual media. Newer vehicles have range of systems which allows the devices and even the laptops to connect to the vehicle for the passenger use. Many In Vehicle System allows the passengers to communicate with the machine of the system. The automotive industry is one of the top adopters of the technical innovations for various  purposes like enhancing engine working, security purposes and even the delivery economic services. The datasets like the color, images, hand movements as what they represent are fed within the development of the automotive system. Different features can be extracted with help of machine learning technology used. The features can include automatic gear system, automatic music player or AC system. . As the appearance of hands varies among regions and the the techniques can be implemented within the machine learning. The accuracy of the hand gestures  should be bounded to avoid the confusions and should provide accurate infotainment system. All the technical restrictions should be controlled. The system should be prepared to react as soon as the input is given. 

    Driver’s distraction has lead to the various accidents and also there has been extensive physiological research done on different levels of distractions in terms of measurable metrics, like total eyes off the road, even various secondary tasks that has shown in the increase which is an contributing factor. The most common example is the usage of the mobile phone while driving which may require visual, manual cognitive attention where the awareness of the driver and reaction capabilities have reduced. Other secondary tasks like reading. Eating or drinking  while driving has also lead to the prone accidents.

The In Vehicle Infotainment system can be used with the standard two way communication with the voice commands. It includes rear seat capability which allows passengers to control the visual media. Newer vehicles have range of systems which allows the devices and even the laptops to connect to the vehicle for the passenger use. Many In Vehicle System allows the passengers to communicate with the machine of the system. The automotive industry is one of the top adopters of the technical innovations for various  purposes like enhancing engine working, security purposes and even the delivery economic services.

    The main purpose of the IVI System implementation is due to decreasing the driver’s distraction which may lead to many accidents. Interaction with the infotainment devices like mobile, GPS, radio, and recently used tablets are used for various purposes. Since infotainment devices are increasing in the presence of new car models, there is also increase in the interest to develop much safer interfaces which helps to allow the drivers their eyes on the road. In last many years, many car systems have been developed to provide the user with the natural way to interact with the infotainment systems. These systems works on main principal categories like buttons or tablets, electronic sensors or camera, voice controls, vision based systems for detection of driver’s actions. The technology is almost available in all the car systems with an clean environment to provoke some degree of stress. Touching based devices are comfortable when they are at the reachable place. 

The hand gestures being dynamic that is changing in the state over time, they can be contrasted against static hand poses which in turn cant be changed over the state, therefore in the vehicle infotainment system, a static hand pose is all that matters. In an vehicle infotainment system, the first audio channel can be done either by giving the inputs as by the numbers with using the fingers or by zooming in and zooming out of the fingers. Our main approach makes a simple fact of dynamic hand gestures  with the distinguishable ending pose. Consequently grabbing movements can be well lowered by hand zooming in and zooming out postures. During the interaction phase, our gesture recognition module takes the snapshots using the camera of the laptop or any other external camera attached. The hand movements are recognised by the camera and the inputs can be fed within the code which can be given as the output.

The hand gestures being dynamic that is changing in the state over time, they can be contrasted against static hand poses which in turn cant be changed over the state, therefore in the vehicle infotainment system, a static hand pose is all that matters. In an vehicle infotainment system, the first audio channel can be done either by giving the inputs as by the numbers with using the fingers or by zooming in and zooming out of the fingers. Our main approach makes a simple fact of dynamic hand gestures  with the distinguishable ending pose. Consequently grabbing movements can be well lowered by hand zoom in in and zooming out postures. During the interaction phase, our gesture recognition module takes the snapshots using the camera of the laptop or any other external camera attached. The hand movements are recognised by the camera and the inputs can be fed within the code which can be given as the output. A static hand recognition by the most frequent recognition within the series should be above threshold that is lower disturbance.