What's the point? Frame-wise Pointing Gesture Recognition with Latent-Dynamic Conditional Random Fields
This work addresses the problem of accurate and reliable pointing gesture recognition in videos for applications like human-computer interaction, though it appears incremental as it builds on existing methods.
The paper tackled the problem of recognizing pointing gestures in video sequences by using Latent-Dynamic Conditional Random Fields for skeleton-based classification, achieving a frame-wise pointing accuracy of about 83% and a false positive detection rate of 0.63% for continuous gesture detection.
We use Latent-Dynamic Conditional Random Fields to perform skeleton-based pointing gesture classification at each time instance of a video sequence, where we achieve a frame-wise pointing accuracy of roughly 83%. Subsequently, we determine continuous time sequences of arbitrary length that form individual pointing gestures and this way reliably detect pointing gestures at a false positive detection rate of 0.63%.