In virtual reality and augmented reality systems, predictive tracking technology can predict the next direction and/or position of an object or a part of the body. To give you an example, when your head is turning in a certain direction, you can synchronize to predict where your hand might be placed. A common application of predictable tracking technology is to reduce the "motion photon" delay time, which is the time when the user completes an action in a virtual reality or augmented reality environment until the action is fully reflected on the display. . Since the motion itself will produce some delay, when the motion is completed and the information is displayed on the screen, there will be a delay (see below for the reason for the delay), so when you can predict the next direction and position, Then prioritize the relevant data to the screen to greatly reduce the perceived delay. Although there are many concerns about predictable tracking technology in virtual reality applications, in fact, this technology is also very important in the field of augmented reality, especially when the user moves instantaneously in the real world, and the corresponding augmented reality overlay Reflected on the display. For example, if you bring an augmented reality helmet and see the enhanced display overlay on top of the solid object, more importantly, even if you rotate your own head library, the "coverage" in the augmented reality environment needs Still "locking" on the original object, rather than turning as your head turns, it actually shows a part of the real world. The object may be recognized by the camera, but the camera may take some time to capture the frame so that the processor can determine the position of the object in the frame, and then the graphics processing chip needs to re-render the new position on the overlay. By using predictable tracking technology, motion processing can be reduced in overlays compared to the real world. If you see a car driving at a constant speed, then you want to predict where the car might reach after the next second seems to be a very simple matter, and without any surprise, the accuracy of the prediction is also It will be very high - because you not only know the exact location of the car, but also know its current (or estimated) speed, so with these conditions, you can basically infer the future of the car. Where is the location. Of course, if you want to predict where the car will be able to drive in a second, then this prediction may not be 100% accurate every time, because during this time, the car may change direction, or it may accelerate. So for car driving forecasts, the farther you try to predict, the more inaccurate the forecast will be. Simply put, if you let the car predict where the car is in a second, the accuracy is definitely higher than the position where you predicted the car after one minute. In addition, the more you know about the car itself, the greater the chances of predicting accuracy. For example, you not only measure the speed of the car, but also measure its acceleration, and then make more accurate predictions. Therefore, if you can get more behavior information of the object being tracked, you can also improve the prediction accuracy. By way of example, when you perform head tracking, if you can understand the speed of the head rotation and the angle of possible rotation, you can further optimize the tracking model to make a more accurate judgment. Similarly, if you are doing eye tracking, you can also use the acquired eye tracking information to predict head movement (this will be discussed in detail later in this article). Patent Vape,E-Cigarette Vape Pens,E-Cigarette Vaporizer Pen,Disposable Vape Puff Bar Guangzhou Yunge Tianhong Electronic Technology Co., Ltd , https://www.e-cigarettesfactory.com
