Real-Time Hand Shape Classification
This work addresses the challenge of real-time hand pose estimation for applications requiring fast processing, though it appears incremental as it builds on existing methods.
The paper tackles real-time hand shape classification by proposing a parallel framework that combines shape contexts and appearance-based techniques, achieving improved classification scores and demonstrating efficacy through speedup and efficiency analyses.
The problem of hand shape classification is challenging since a hand is characterized by a large number of degrees of freedom. Numerous shape descriptors have been proposed and applied over the years to estimate and classify hand poses in reasonable time. In this paper we discuss our parallel framework for real-time hand shape classification applicable in real-time applications. We show how the number of gallery images influences the classification accuracy and execution time of the parallel algorithm. We present the speedup and efficiency analyses that prove the efficacy of the parallel implementation. Noteworthy, different methods can be used at each step of our parallel framework. Here, we combine the shape contexts with the appearance-based techniques to enhance the robustness of the algorithm and to increase the classification score. An extensive experimental study proves the superiority of the proposed approach over existing state-of-the-art methods.