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TrackEye : Real-Time Tracking Of Human Eyes Using a Webcam

TrackEye : Real-Time Tracking Of Human Eyes Using a Webcam
Introduction Eyes are the most important features of the human face. So effective usage of eye movements as a communication technique in user-to-computer interfaces can find place in various application areas. Eye tracking and the information provided by the eye features have the potential to become an interesting way of communicating with a computer in a human-computer interaction (HCI) system. So with this motivation, designing a real-time eye feature tracking software is the aim of this project. The purpose of the project is to implement a real-time eye-feature tracker with the following capabilities: RealTime face tracking with scale and rotation invariance Tracking the eye areas individually Tracking eye features Eye gaze direction finding Remote controlling using eye movements Instructions to Run and Rebuild TrackEye Installation Instructions Extract file. Settings to be Done to Perform a Good Tracking Settings for Face & Eye Detection Settings for Snake History Related:  eye trackingprograms

actionscript 3 - Flash library for eyes tracking Human Emotion Detection from Image Download source - 2.46 MB Introduction This code can detect human emotion from image. First, it takes an image, then by skin color segmentation, it detects human skin color, then it detect human face. How Does It Work? Skin Color Segmentation For skin color segmentation, first we contrast the image. Then, we have to find the largest connected region. Face Detection For face detection, first we convert binary image from RGB image. Then, we try to find the forehead from the binary image. In the figure, X will be equal to the maximum width of the forehead. Eyes Detection For eyes detection, we convert the RGB face to the binary face. Then we find the starting high or upper position of the two eyebrows by searching vertical. Lip Detection For lip detection, we determine the lip box. So, for detection eyes and lip, we only need to convert binary image from RGB image and some searching among the binary image. Apply Bezier Curve on Lip In the lip box, there is lip and may be some part of nose. History

Welcome to Jinja2 — Jinja2 2.7.2 documentation Jinja2 is a modern and designer friendly templating language for Python, modelled after Django’s templates. It is fast, widely used and secure with the optional sandboxed template execution environment: <title>{% block title %}{% endblock %}</title><ul>{% for user in users %} <li><a href="{{ user.url }}">{{ user.username }}</a></li>{% endfor %}</ul> Features: sandboxed executionpowerful automatic HTML escaping system for XSS preventiontemplate inheritancecompiles down to the optimal python code just in timeoptional ahead of time template compilationeasy to debug. Line numbers of exceptions directly point to the correct line in the template.configurable syntax Additional Information If you can’t find the information you’re looking for, have a look at the index or try to find it using the search function:

PyGaze | projects Welcome to the PyGaze projects page! Here you will find all sorts of information, source code, and demonstrations of the stuff that we're currently working on at PyGaze HQ. We love to share our interest in science and technology with you! Most of this stuff is closely related to PyGaze, and gives a good idea of how you can use the toolbox. current projects PyGazeAnalyser Analysis of eye-tracking data without having to buy an expensive software package, or relying on a commercial party? eye tracker An eye tracker needn't be expensive! mantis shrimp This isn't really a project, but more of an homage to a creature with a truly incredible pair of eyes. news Sun. 2 March 2014 We have added a new project, and it's a big one! Sun. 12 January 2014 Because we can write about whatever we please: an ode to the mantis shrimp!

ITU Gaze Tracker The ITU Gaze Tracker is an open-source eye tracker that aims to provide a low-cost alternative to commercial gaze tracking systems and to make this technology more accessible. It is developed by the Gaze Group at the IT University of Copenhagen and other contributors from the community, with the support of the Communication by Gaze Interaction Association (COGAIN). The eye tracking software is video-based, and any camera equipped with infrared nightvision can be used, such as a videocamera or a webcam. The cameras that have been tested with the system can be found in our forum. We encourage users and developers to test our software with their cameras and provide feedback so we can continue development. The ITU Gaze Tracker is hosted in SourceForge. In order to run the software, uncompress the zip file and double click on GazeTrackerUI.exe. The user's guide to run and configure the ITU Gaze Tracker can be downloaded from here (PDF document) The requirements to run the ITU Gaze Tracker are: » Realtime Terminator Salvation "Machine Vision" fx Have you seen Terminator Salvation yet? There's a bunch of cool visual effects developed by Imaginary Forces, it shows the world as seen by machines. There's a lot of object tracking going on there, I was thinking whether I could recreate the whole thing just in pure AS3. And, well, here's the result (which I am actually very proud of) ;-) Click image to activate, wait for the video to buffer (1.6MB) press EDIT button to play with the filters (in full screen mode). Enable your webcam (if you have one) and play about with sliders and checkboxes – try if your face can be tracked too – but then watch for evil Terminators – they'll come and get you! This is a part of the whole video filter framework I am developing just now, the inspiration came from Joa Ebert's Image Processing library (as far as I know, he's cooking a complete rewrite). My approach is to make everything as much simple as I can. The face tracking is actually relatively simple, I will briefly describe each step:

Motion Detection Algorithms Introduction There are many approaches for motion detection in a continuous video stream. All of them are based on comparing of the current video frame with one from the previous frames or with something that we'll call background. In this article, I'll try to describe some of the most common approaches. In description of these algorithms I'll use the AForge.NET framework, which is described in some other articles on Code Project: [1], [2]. So, if you are common with it, it will only help. The demo application supports the following types of video sources: AVI files (using Video for Windows, interop library is included); updating JPEG from internet cameras; MJPEG (motion JPEG) streams from different internet cameras; local capture device (USB cameras or other capture devices, DirectShow interop library is included). Algorithms One of the most common approaches is to compare the current frame with the previous one. The simplest motion detector is ready! Here is the result of it: Conclusion

Community Weekend Project: Take a Tour of Open Source Eye-Tracking Software Right this very second, you are looking at a Web browser. At least, those are the odds. But while that's mildly interesting to me, detailed data on where users look (and for how long) is mission-critical. The categories mentioned above do a fairly clean job of dividing up the eye-tracking projects. For example, there are eye-tracking projects designed to work with standard, run-of-the-mill Web cams (like those that come conveniently attached to the top edge of so many laptops), and those meant to be used with a specialty, head-mounted apparatus. Many projects have a particular use-case in mind, but with the ready availability of Webcams, developers are exploring alternative uses suitable for gaming, gesture-input, and all sorts of crazy ideas. Tracking Eye Movement With a Webcam On the inexpensive end of the hardware spectrum are those projects that implement eye-tracking using a standard-issue Webcam. OpenGazer is by far the simplest such project to get started with. Looking Ahead

Eye tracking Scientists track eye movements in glaucoma patients to check vision impairment while driving. Yarbus eye tracker from the 1960s. History[edit] In the 1800s, studies of eye movement were made using direct observations. In 1879 in Paris, Louis Émile Javal observed that reading does not involve a smooth sweeping of the eyes along the text, as previously assumed, but a series of short stops (called fixations) and quick saccades.[1] This observation raised important questions about reading, questions which were explored during the 1900s: On which words do the eyes stop? An example of fixations and saccades over text. Edmund Huey[2] built an early eye tracker, using a sort of contact lens with a hole for the pupil. The first non-intrusive eye-trackers were built by Guy Thomas Buswell in Chicago, using beams of light that were reflected on the eye and then recording them on film. In the 1950s, Alfred L. In the 1970s, eye-tracking research expanded rapidly, particularly reading research.

AS3 Webcam Motion Tracking › Detecting and Tracking an Objects Movement in Flash Update Ok, you can now grab the MotionTracker source code (AS2 & AS3). Version 2 eventually will include the other methods for detecting and tracking motion which I mentioned. For now I have just included code for the technique used in the demo. Download: AS3 Webcam Motion Tracking For those of you without access to a webcam (and as an example of a practical use for this class) here is a short video demonstrating the program I wrote for the installation piece. End of update Webcam required to view the demo (obviously…) I’m currently working on putting up a show, part of which will be a live generative piece, constructed from the movement of visitors in the gallery space. Anyway; I researched the concept of motion tracking, and made some notes on my own ideas. One of the most attractive ideas was to divide the screen into a grid, and average the colours within each segment at regular intervals. Here’s how it works So that takes care of the motion detection, but what about the tracking?