Showing 4 open source projects for "gaze tracking opencv"

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  • 1
    OpenCV

    OpenCV

    Open Source Computer Vision Library

    OpenCV (Open Source Computer Vision Library) is a comprehensive open-source library for computer vision, machine learning, and image processing. It enables developers to build real-time vision applications ranging from facial recognition to object tracking. OpenCV supports a wide range of programming languages including C++, Python, and Java, and is optimized for both CPU and GPU operations.
    Downloads: 14 This Week
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  • 2
    Computer Vision in Action

    Computer Vision in Action

    A computer vision closed-loop learning platform

    Computer Vision in Action is a practical, example-rich repository that demonstrates real-world applications of computer vision techniques and algorithms in Python, often using OpenCV, deep learning models, and related tooling. It serves as a hands-on companion for learners and engineers who want to understand not just the theory, but how computer vision is actually implemented for tasks like object detection, image classification, feature tracking, optical flow, and image segmentation. The repository includes structured code examples, scripts, and notebooks that cover pipeline construction, preprocessing, model inference, and visual output rendering, making it easy for newcomers or intermediate practitioners to adapt patterns to their own projects. ...
    Downloads: 0 This Week
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  • 3
    opencv4nodejs

    opencv4nodejs

    Nodejs bindings to OpenCV 3 and OpenCV 4

    OpenCV4NodeJS is a Node.js binding for OpenCV, allowing developers to integrate computer vision capabilities directly into JavaScript applications for image processing, object detection, and facial recognition.
    Downloads: 0 This Week
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  • 4
    OpenTLD

    OpenTLD

    OpenTLD is an open source library for real-time 2D tracking

    OpenTLD is an open source implementation of the TLD (Tracking-Learning-Detection) framework, designed for real-time 2D tracking of a single object in video sequences. Because it fuses tracking and detection, TLD can recover from occlusions, drift, or failures by using its detection mechanism to reacquire the object. In terms of usage, one typically initializes the tracker by providing a bounding box on the first frame, then calls a function like run_TLD to process a video and obtain bounding...
    Downloads: 0 This Week
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