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Plastic deformation represents a fundamental concept in materials science, which explains the irreversible change in the shape of a material when it experiences stress beyond its elastic capability. This phenomenon is important in structural engineering, especially in designing and analyzing cantilever beams—structures that are securely fixed at one end and bear loads at the opposite end. When these beams are subjected to loads within their elastic range, they will return to their...
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It is essential to understand how structural members behave under plastic deformation when the bending stress exceeds the material's yield strength. This state of deformation permanently alters the shape of the member, in contrast to the linear elastic behavior observed before yielding. The strain at any point in the member is expressed in terms of maximum strain. Notably, the neutral axis, which coincides with the centroid during elastic bending, shifts away from the centroid under plastic...
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Tracking Morphogenetic Tissue Deformations in the Early Chick Embryo
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Deformable Object Tracking With Gated Fusion.

Wenxi Liu, Yibing Song, Dengsheng Chen

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    This study introduces deformable convolution to improve object tracking by enhancing appearance representations. The new method effectively handles severe appearance variations, outperforming existing tracking-by-detection approaches.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Deep Learning

    Background:

    • The tracking-by-detection framework, enhanced by Convolutional Neural Networks (CNNs), is widely used.
    • Existing methods struggle with severe object appearance variations due to fixed-grid convolutional operations.
    • Object pose changes and environmental variations challenge traditional tracking accuracy.

    Purpose of the Study:

    • To enhance target appearance representation in tracking-by-detection frameworks.
    • To address limitations of fixed-grid convolutions in handling appearance variations.
    • To improve the robustness and accuracy of object tracking.

    Main Methods:

    • Introduction of a deformable convolution layer to enrich target appearance features.
    • Adaptive enhancement of original features using deformable convolution.
    • Implementation of a gated fusion scheme to control feature variation influence.

    Main Results:

    • Deformable convolution adaptively captures and enhances target appearance variations.
    • Enriched features improve the discriminative ability of CNN classifiers for target objects and backgrounds.
    • Proposed tracker demonstrates superior performance against state-of-the-art methods on standard benchmarks.

    Conclusions:

    • Deformable convolution effectively enriches appearance representations for robust object tracking.
    • The proposed gated fusion scheme refines the impact of appearance variations.
    • The enhanced framework significantly improves tracking performance, especially under challenging conditions.