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Real-time detection with webcam

By Dmitry Batenkov, June 2010

Tags: General

OpenCV is an open-source, cross-platform library for real-time computer vision. Originally developed by Intel, the library will use Intel's Integrated Performance Primitives, if it is found on the system. It is very well-documented with a full reference manual, many examples and tutorials, and a book [which is also a good introduction to computer vision]. Interfaces for C, C++, and Python are also available in OpenCV.

Example applications of the OpenCV library include human-computer interaction; object identification, segmentation and recognition; face recognition; gesture recognition; motion tracking, ego motion, motion understanding; structure from motion [SFM]; stereo and multi-camera calibration and depth computation; and mobile robotics.

In this tutorial, we will learn how to do real-time face detection using a webcam. We will utilize a machine-learning object detection algorithm known as the Viola-Jones detector. It's a fast classification mechanism using Haar-like wavelet features. OpenCV ships with a very good "classifier file" for faces, but one can also train the classifier to recognize any kind of objects.


First, download the latest OpenCV release for your platform from and install it.

"In this tutorial, we will learn how to do real-time face detection using a webcam. We will utilize a machine-learning object detection algorithm known as the Viola-Jones detector."

Next, copy the attached program to a file named You can also download it from

In the downloaded source archive, locate the classifier file data/haarcascades/haarcascade_frontalface_alt_tree.xml and replace the placeholder in the code with this original location.

Make sure that the Python interpreter knows the location for the OpenCV Python bindings. In Linux, it should be set automatically. In Windows, set the environment variable

set pythonpath = \Python2.6\Lib\site-package.

Now, connect your webcam and run the program: python

To exit, press Esc. Have fun!


Once an object is detected, we can start tracking it. OpenCV has an implementation for CamShift tracking algorithm. [See the example on]

Add detection of the eyes, mouth, and so on. [OpenCV ships with corresponding classifiers.] You can recognize emotions! See the video:

If you replace the face classifier with hands classifier, and add tracking, you can now recognize gestures!
—Dmitry Batenkov


Object identification links
Viola-Jones algorithm

Haar training tutorial

Haar cascades repository

HCI Projects Using OpenCV Handvu

Gesture recognition

EHCI Head tracking

PyEyes Eyes tracking

CCV/touchlib Multi-touch library

Other HCI/CV Toolkits

TUIO Common API for tangible multitouch surfaces (list of implementations)

Trackmate Do-it-yourself tangible tracking system

Sphinx Speech recognition toolkit

VXL versatile computer vision libraries

Integrating Vision Toolkit





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Code: Real-Time Detection with Webcam

Code available here

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