![]() ![]() ![]() Since the Arduino is capable of providing enough power for two servos, there's not much to the assemble. Now that the camera jig is set up we need to assemble the electronics. To find the mid-point of the pan/tilt range of motion just manually move the bracket from side to side, and up and down and approximate the center position for each axis. I wanted the camera to have the widest range of motion possible when tracking, so I found the mid-point of the pan and tilt angles, and then mounted my camera so that the lens was facing forward. Don't be afraid of a little Duct Tape! One thing to check before mounting the camera to the bracket, though, is the range of motion of the pan/tilt. If you don't have the same webcam you'll have to find your own way to mount the webcam to the pan/tilt bracket. After making the holes bigger I just mounted both swivels to one edge of the bracket, and then with a little bit of wiggling I got the webcam back onto the swivels. Unfortunately the screws were just a tad too big to fit through the holes in the swivel, so I enlarged them a bit with a drill press. I found some small screws to mount the swivel to the bracket. Since there are a ton of cut-outs in the pan/tilt bracket it wasn't too difficult. The only thing left was to figure out how to mount the swivels onto the bracket in a fashion that would allow the camera to be put back onto the swivels. After taking out a handful of screws, and pulling apart some rather reluctant plastic, I was lucky to find that there were some small metal swivels that I could mount the camera with. I figured that the camera had to be mounted onto it's bracket somehow, so if I could take it out of the current bracket it might make it easier to mount to the pan/tilt bracket. It came on this little mounting swivel so that you can hang it from the top of a monitor. I'm using a Logitech Webcam that we lying around the office. Once the Pan/Tilt Bracket has been assembled we need to find a way to mount the webcam onto the bracket. This will show you how to put the bracket together and install the servos for controlling the bracket's orientation. Start by putting the Pan/Tilt Bracket together using the assembly guide from the product page. There are several pieces for this project that need to be assembled. Arduino Uno (or other 5V Arduino Compatible board).A video of the final product illustrates the concept a little better than I can explain it. ![]() In this tutorial we're going to look at how to use OpenCV, a real time computer vision library, with Processing, Arduino, a webcam and a pan/tilt bracket to create a video that will keep a persons face in the middle of the frame as they walk around the room. Here is the entire list of tutorials which will walk you through the basics of OpenCV with simple example programs.Face Tracking with a Pan/Tilt Servo Bracket I have tested all example programs in this tutorial with OpenCV 3.3.1 and Microsoft Visual Studio 2015. Therefore, I decided to prepare this tutorial from the very basic concepts of image processing and computer vision providing simple examples of OpenCV C++ programs with illustrations. And there are few tutorials which can be found on OpenCV for beginners in the internet. OpenCV applications run on Windows, Android, Linux, Mac and iOSĪlthough OpenCV is a powerful tool which can be used to develop complex image processing and computer vision applications, the documentation of OpenCV is not enough for a newbie to learn OpenCV by himself/herself.There are also C, Python and JAVA full interfaces.Optimized for real time image processing & computer vision applications.Now it has several hundreds of inbuilt functions which implement image processing and computer vision algorithms which make developing advanced computer vision applications easy and efficient. It is a library mainly aimed at real time processing. Therefore you can use the OpenCV library even for your commercial applications. It is free for both commercial and non-commercial use. OpenCV is an open source C++ library for image processing and computer vision, originally developed by Intel, later supported by Willow Garage and and is now maintained by Itseez. Basic steps for a typical computer vision application as follows. In other words, computer vision is making the computer see as humans do. Computer vision which go beyond image processing, helps to obtain relevant information from images and make decisions based on that information. ![]()
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