Hi, my name is Dinyakalal and I'm a researcher focusing on visual object tracking. This result is the result of my PhD thesis where I investigated how to track an unknown object in a video sequence and how to improve the tracking performance during tracking. We have developed an algorithm which we call predator. There's any other tracker predator estimates the object location and scale changes, but in addition it learns how the object looks and what is not an object. And using this information it scans every frame searching for the object. If the object disappears and later comes back we are able to detect it. This combination of tracking, learning and detection is unique and therefore opens completely new possibilities for a number of industrial applications. First of them is in human computer interfaces. Consider you want to make an interface where the user catches and drags something such as a mouse. So using our system it's possible to select the object which you want to track by a bounding box and that's it. Now the system is learning how the gesture looks like in different scales, how it looks in different rotations and so on and after a while we have pretty reliable system which is acting as a virtual mouse. The nice property is that over time it's getting better and better so the user has the flexibility to create his own gesture. Another example is in a face recognition. If I select my face for example and start tracking immediately after the system can recognize me, recognize me when I come back. And so we are talking about some games where this can be used or maybe very simple security systems where the face can be recognized among other faces. The same functionality can be used to improve visual search in a Google application. The images are queried by a single image. However, if you have a tracker like this one the user can send a query of number of images and improve the search performance. So these are just couple of ideas on what the parent can do. And if you are still interested please watch the videos at the end or go to my website where I explain a little bit more about it. Thank you very much and see you later.