What is OpenCV tracking?
Computer vision library with open source code is the full name for OpenCV, programming functions of the library and a set of tools with open source code for use between platforms in real time in real time processing of the computer and monitoring OpenCV. Developed at the turn of the 21st century, was originally a purpose for three-dimensional (3-D) display walls and rays. Opencv, which uses creative encoding, can offer a framework for a vision based on vision optimized in C or C ++, even if available in several languages and is customizable for remote use on manual devices. It is able to capture video files in real time, basic video configuration, object detection and movement and color monitoring, among other things. Opencv is capable of calibrating the camera because it can find and watch the camera calibration and set stereo correspondence on video cameras.HYBU, Return results in degrees and recording subsequent shifts. The ultimate result would be the sum of the original orientation and angles of the shift. Reading and writing image files and their Nuci to their three -channel color image can be edited, directly and indirectly accessible and converted to images in gray or color bytes.
The optical flow of images can be directed by monitoring the comparison of blocks and each pixel calculated and instructed in the flow. Assign and release of images for single -channel bytes or three -channel float images to set up an area of interest or cloning is possible. OpenCV allows you to capture the frame images from the video sequence from a file from several cameras simultaneously by grabing one image from each, and then searching g of all, creating and editing new video flows.
OpenCV's face monitoring is performed using its Camshift functions. This function implementsThe object monitoring algorithm, finds an object center, creates a color histogram, calculates the probability of the face, and then moves the location of the facial rectangle in each video, and makes adjustments by calculating the size and angle. It concentrates the brightest pixels over the center face and uses a scale to adapt to smaller faces in subsequent images if the image is retreating.
OpenCV tracking capabilities are used in many applications. From facial recognition to gesture recognition, mobile robotics, interaction with human computer and stereopsis, which creates a perception of the depth of stereo vision using two cameras, use of object, color and monitoring of movement. OpenCV also has statistical libraries of machine learning containing modules for learning a tree decision -making, algorithms monitoring maximization of expectations of maximization, gradientous trees and artificial neural networks functioning modules.