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Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Curvilinear Motion: Rectangular Components01:23

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Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
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Curvilinear Motion: Polar Coordinates01:27

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In polar coordinates, the motion of a particle follows a curvilinear path. The radial coordinate symbolized as 'r,' extends outward from a fixed origin to the particle, while the angular coordinate, 'θ,' measured in radians, represents the counterclockwise angle between a fixed reference line and the radial line connecting the origin to the particle.
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Related Experiment Video

Updated: Jun 26, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Semantic-Driven Generation of Hyperlapse from 360 Degree Video.

Wei-Sheng Lai, Yujia Huang, Neel Joshi

    IEEE Transactions on Visualization and Computer Graphics
    |September 15, 2017
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    Summary

    This study introduces a novel system for transforming 360-degree videos into normal field-of-view (NFOV) hyperlapses. The system uses visual saliency and semantics for optimized sampling, enhancing the viewer experience.

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    Area of Science:

    • Computer Vision
    • Digital Media Processing
    • Human-Computer Interaction

    Background:

    • Panoramic (360-degree) videos offer immersive experiences but can be challenging to navigate.
    • Hyperlapse videos condense time and space, providing dynamic overviews.
    • Current methods for generating hyperlapses from 360-degree content may not fully leverage visual importance or user preferences.

    Purpose of the Study:

    • To develop an automated system for converting 360-degree videos into normal field-of-view (NFOV) hyperlapses.
    • To enhance the viewing experience by employing visual saliency and semantic understanding for non-uniform spatial and temporal sampling.
    • To enable user customization of hyperlapses by allowing selection of objects of interest.

    Main Methods:

    • Video stabilization of input 360-degree footage.
    • Computation of regions of interest and saliency scores.
    • Optimization of saliency and motion smoothness for initial hyperlapse generation.
    • Saliency-aware frame selection and adaptive 2D video stabilization for final output.

    Main Results:

    • A novel system capable of generating NFOV hyperlapses from 360-degree videos.
    • Demonstrated effectiveness across diverse scenes.
    • User study results indicate competitive or superior performance compared to state-of-the-art methods.

    Conclusions:

    • The proposed system effectively converts 360-degree videos into perceptually optimized NFOV hyperlapses.
    • Exploiting visual saliency and semantics significantly improves hyperlapse quality and user engagement.
    • The system offers a valuable tool for content creators and viewers seeking enhanced video experiences.