End-to-end bare-hand localization system for human–computer interaction: a comprehensive analysis and viable solution.

Accurately localizing bare-hands is crucial for human–computer interaction systems. In real-time systems, computational time plays a pivotal role in achieving this task within a specified timeframe. The localization process may involve detection, tracking, or both, depending on the framework's needs. Most studies on bare-hand detection and tracking have been conducted in controlled environments. However, localizing bare-hands in uncontrolled environments are challenging due to the complexity of variations, such as changes in illumination, rotation, occlusion, scale, pose, speed, and impostor bare-hands. These factors can significantly impact the performance of the models, along with background feature domination effect and motion blur, which further complicate localization. To address these challenges, this paper presents a comprehensive analysis of the most significant deep learning-based hand localization models. We have customized an object detector as a bare-hand localization model by incorporating detection and tracking modules, providing computationally efficient performance while addressing the variations. To evaluate the models' efficiency across a range of scales, we have proposed a scale-based bare-hand database with varying scales from 20 to 200 cm. We have also evaluated these models on various bare-hand benchmark databases. [ABSTRACT FROM AUTHOR]