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Finger tracking of two pianists' fingers playing the same piece (slow motion, no sound) [1]. In the field of gesture recognition and image processing, finger tracking is a high-resolution technique developed in 1969 that is employed to know the consecutive position of the fingers of the user and hence represent objects in 3D.
The first alpha version of OpenCV was released to the public at the IEEE Conference on Computer Vision and Pattern Recognition in 2000, and five betas were released between 2001 and 2005. The first 1.0 version was released in 2006. A version 1.1 "pre-release" was released in October 2008. The second major release of the OpenCV was in October 2009.
A library to allow development of marker-based, Natural Feature Tracking and location-based AR applications on the web. It can be used in conjunction with A-Frame (virtual reality framework) or three.js: MindAR: 2021 [19] MIT: A library to allow development of image-tracking and face-tracking types of AR applications on the web.
Motion capture of two pianists' right hands playing the same piece (slow-motion, no-sounds) [1] Two repetitions of a walking sequence recorded using motion capture [2]. Motion capture (sometimes referred as mo-cap or mocap, for short) is the process of recording the movement of objects or people.
Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]
An example of emerging gesture-based motion capture is skeletal hand tracking, which is being developed for virtual reality and augmented reality applications. An example of this technology is shown by tracking companies uSens and Gestigon, which allow users to interact with their surroundings without controllers. [20] [21] Wi-Fi sensing [22]
OpenCV provides a comprehensive set of functions that can support real-time computer vision applications, such as image recognition, motion tracking, and facial detection. [69] Originally developed by Intel , OpenCV has become one of the most popular libraries for computer vision due to its versatility and extensive community support.
ARToolKit is an open-source computer tracking library for creation of strong augmented reality applications that overlay virtual imagery on the real world. Currently, it is maintained as an open-source project hosted on GitHub. [2]