Indoor Multiperson Detection and Recognition Through Footsteps: A Deep Learning Approach With Acoustic Signal Analysis

In the era of intelligent environments, detecting and recognizing multiperson is crucial across various applications such as smart homes, security surveillance, and human-computer interaction. This article introduces a novel methodology integrating acoustic signal processing with deep learning to identify and detect multipersons based on indoor footsteps. Anchored in a simulated family setting of three individuals, the research establishes a comprehensive audio repository, demonstrating the efficacy of the proposed approach. Presently, the study focuses on family recognition and activity detection, which can be applied to prevent unauthorized intrusions and facilitate the issuance of intelligent commands based on identified activities, thereby enhancing the convenience of daily life. Furthermore, this article outlines prospective avenues for refining and expanding this pioneering methodology, opening new vistas for future research endeavors in this domain.