High-Speed Hand Tracking for Studying Human-Computer Interaction

Understanding how a human behaves while performing human-computer interaction tasks is essential in order to develop better user interfaces. In the case of touch and gesture based interfaces, the main interest is in the characterization of hand movements. The recent developments in imaging technology and computing hardware have made it attractive to exploit high-speed imaging for tracking the hand more accurately both in space and time. However, the tracking algorithm development has been focused on optimizing the robustness and computation speed instead of spatial accuracy, making most of them, as such, insufficient for the accurate measurements of hand movements. In this paper, state-of-the-art tracking algorithms are compared based on their suitability for the finger tracking during human-computer interaction task. Furthermore, various trajectory filtering techniques are evaluated to improve the accuracy and to obtain appropriate hand movement measurements. The experimental results showed that Kernelized Correlation Filters and Spatio-Temporal Context Learning tracking were the best tracking methods obtaining reasonable accuracy and high processing speed while Local Regression filtering and Unscented Kalman Smoother were the most suitable filtering techniques.