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Training a real-time gesture recognition model heavily relies on annotateddata. However, manual data annotation is costly and demands substantial humaneffort. In order to address this challenge, we propose a framework that canautomatically annotate gesture classes and identify their temporal ranges. Ourframework consists of two key components: (1) a novel annotation model thatleverages the Connectionist Temporal Classification (CTC) loss, and (2) asemi-supervised learning pipeline that enables the model to improve itsperformance by training on its own predictions, known as pseudo labels. Thesehigh-quality pseudo labels can also be used to enhance the accuracy of otherdownstream gesture recognition models. To evaluate our framework, we conductedexperiments using two publicly available gesture datasets. Our ablation studydemonstrates that our annotation model design surpasses the baseline in termsof both gesture classification accuracy (3-4% improvement) and localizationaccuracy (71-75% improvement). Additionally, we illustrate that thepseudo-labeled dataset produced from the proposed framework significantlyboosts the accuracy of a pre-trained downstream gesture recognition model by11-18%. We believe that this annotation framework has immense potential toimprove the training of downstream gesture recognition models using unlabeleddatasets.

Boosting Gesture Recognition with an Automatic Gesture Annotation Framework