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    Recognizing individuals by their walking gait becomes difficult when multiple people walk together. This study introduces a new model that effectively identifies a person's unique gait even in crowded scenarios, improving multi-person gait recognition.

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    Area of Science:

    • Biometrics
    • Computer Vision
    • Pattern Recognition

    Background:

    • Gait recognition is crucial for biometrics, but existing methods struggle with multiple individuals walking together.
    • A person's gait characteristics can change when walking in a group, posing a significant challenge for recognition systems.

    Purpose of the Study:

    • To propose a novel attribute discovery model for recognizing individuals by their gait while walking among multiple people.
    • To enhance multi-gait recognition performance in complex, crowded environments.

    Main Methods:

    • Integration of human graphlets with tracking-by-detection for complete silhouette extraction.
    • Development of stable and discriminative attributes using a latent conditional random field (L-CRF) model.
    • Training within a latent structural support vector machine (SVM) framework with a novel constraint for improved multi-gait recognition.

    Main Results:

    • The proposed attribute discovery model effectively identifies individual gait patterns in multi-person walking scenarios.
    • Experimental results show superior recognition performance compared to traditional gait recognition methods in group walking conditions.
    • The method successfully extracts gait features based on dense trajectories for accurate recognition.

    Conclusions:

    • The developed model offers a robust solution for gait recognition in challenging multi-person scenarios.
    • This research advances the field of biometrics by addressing the limitations of single-person gait analysis.
    • The proposed approach demonstrates significant improvements in recognizing individuals based on their gait when walking collectively.