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Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

549
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
549
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

456
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.
Here, in order to determine the magnitude of velocity and acceleration for point...
456
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

278
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.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
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Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

446
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
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Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

409
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
409
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

442
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Related Experiment Video

Updated: Sep 21, 2025

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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Motion Feature Aggregation for Video-Based Person Re-Identification.

Xinqian Gu, Hong Chang, Bingpeng Ma

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 27, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an efficient Motion Feature Aggregation (MFA) method to improve video-based person re-identification by effectively extracting motion features. MFA enhances re-identification accuracy, especially when appearance changes significantly.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Video-based person re-identification (re-id) primarily relies on appearance features, often neglecting crucial motion information.
    • Existing methods struggle to extract motion features effectively and efficiently for video re-id tasks.
    • Motion features are vital for distinguishing individuals, particularly in scenarios with significant appearance variations.

    Purpose of the Study:

    • To propose an efficient Motion Feature Aggregation (MFA) method for modeling and aggregating motion information at the feature map level for video-based re-id.
    • To enhance the performance of video-based person re-identification by integrating complementary motion and appearance features.
    • To develop a method that can be easily combined with existing network architectures for end-to-end training.

    Main Methods:

    • The proposed Motion Feature Aggregation (MFA) method comprises two modules: coarse-grained motion learning (based on body part position changes) and fine-grained motion learning (based on body part appearance changes).
    • These modules capture motion information at different granularities, providing complementary insights.
    • The MFA method is designed for seamless integration into existing deep learning architectures for video re-id.

    Main Results:

    • Experiments on four benchmark datasets confirm that motion features extracted by MFA significantly complement appearance features in video-based re-id.
    • The MFA method demonstrates particular effectiveness in scenarios involving substantial appearance changes.
    • State-of-the-art results were achieved on the LS-VID dataset, the largest publicly available dataset for video-based re-id, surpassing previous methods by a considerable margin.

    Conclusions:

    • Motion features are essential for robust video-based person re-identification and effectively address limitations of appearance-only methods.
    • The proposed MFA method offers an efficient and effective approach to incorporate motion dynamics into video re-id systems.
    • The MFA method represents a significant advancement in video-based person re-identification, achieving superior performance on challenging datasets.