On this workshop, we are going to get acquainted with the basics of the computer vision theory, look into an open source computer vision library OpenCV, study algorithms of motion detection and implement our first application for the smart home.
Retail (c) • 3D model building • Medical imaging (d) • Automotive safety (e) • Match move • Motion capture • Surveillance (f) • Fingerprint recognition and biometrics
(a) image blending (b) shape from shading (c) edge detection (d) physically based models (e) regularisation-based surface reconstruction (f) range data acquisition and merging
in Nizhny Novgorod (Russia) • later supported by Willow Garage and is now maintained by Itseez • In August 2012, support for OpenCV was taken over by a non-profit foundation OpenCV.org
algorithms (both classic and state-of-the-art computer vision and machine learning algorithms) • more than 47,000 people of user community • C++, C, Python, Java and MATLAB interfaces • supports Windows, Linux, Mac OS, Android and iOS • full-featured CUDA and OpenCL interfaces are being actively developed
and camera systems) • Radio Frequency Energy (radar, microwave and tomographic motion detection) • Sound (microphones and acoustic sensors) • Vibration (triboelectric, seismic, and inertia- switch sensors) • Magnetism (magnetic sensors and magnetometers)
X seconds calculate the middle mass in frame 2 if (mm_frame_1 - mm_frame_2) > threshold) then motion detected or calculate the average of a selected color in frame 1 wait X seconds calculate the average of a selected color in frame 2 if (abs(avg_frame_1 - avg_frame_2) > threshold) then motion detected
to pixel ratio if your camera is at an angle to the horizon or experiences the lens effect • when multiple objects cross over each other the algorithm gets confused which blob is which