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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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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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Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
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Gait-based person recognition using arbitrary view transformation model.

Daigo Muramatsu, Akira Shiraishi, Yasushi Makihara

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 26, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an arbitrary view transformation model (AVTM) to improve gait recognition accuracy across different viewing angles. The enhanced arbitrary view transformation model with part-dependent view selection (AVTM_PdVS) further boosts performance, especially in verification tasks.

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    Area of Science:

    • Computer Science
    • Biometrics
    • Machine Learning

    Background:

    • Gait recognition offers robust person authentication, even with low-resolution imagery.
    • Cross-view matching remains a challenge for view transformation models (VTMs) when target views differ from discrete training views.
    • Existing VTMs struggle with arbitrary viewing angles, leading to reduced accuracy in real-world scenarios.

    Purpose of the Study:

    • To develop an arbitrary view transformation model (AVTM) for accurate gait recognition from arbitrary views.
    • To enhance the AVTM with a part-dependent view selection scheme (AVTM_PdVS) for improved accuracy.
    • To validate the effectiveness of AVTM and AVTM_PdVS on diverse datasets.

    Main Methods:

    • Constructed 3D gait volume sequences from training subjects.
    • Generated 2D gait silhouette sequences by projecting 3D volumes onto target views.
    • Trained the AVTM using extracted gait features from 2D sequences.
    • Extended the model to AVTM_PdVS, incorporating part-dependent view selection for gait features.

    Main Results:

    • The proposed AVTM significantly improves cross-view gait recognition accuracy.
    • The AVTM_PdVS further enhances accuracy, particularly in verification scenarios.
    • Experiments demonstrated effectiveness across different data collection settings.

    Conclusions:

    • The AVTM effectively addresses the challenge of arbitrary view changes in gait recognition.
    • AVTM_PdVS offers a superior approach by adapting view transformation to different body parts.
    • These models represent a significant advancement for robust person authentication using gait biometrics.