Scale-adaptive gesture computing: detection, tracking and recognition in controlled complex environments

Complexity intensifies when gesticulations span various scales. Traditional scale-invariant object recognition methods often falter when confronted with case-sensitive characters in the English alphabet. The literature underscores a notable gap, the absence of an open-source multi-scale un-instructional gesture database featuring a comprehensive dictionary. In response, we have created the NITS (gesture scale) database, which encompasses isolated mid-air gesticulations of ninety-five alphanumeric characters. In this research, we present a scale-centric framework that addresses three critical aspects: (1) detection of smaller gesture objects: our framework excels at detecting smaller gesture objects, such as a red color marker. (2) Removal of redundant self co-articulated strokes: we propose an effective approach to eliminate redundant self co-articulated strokes often present in gesture trajectories. (3) Scale-variant approach for recognition: to tackle the scale vs. size ambiguity in recognition, we introduce a novel scale-variant methodology. Our experimental results reveal a substantial improvement of approximately 16% compared to existing state-of-the-art recognition models for mid-air gesture recognition. These outcomes demonstrate that our proposed approach successfully emulates the perceptibility found in the human visual system, even when utilizing data from monophthalmic vision. Furthermore, our findings underscore the imperative need for comprehensive studies encompassing scale variations in gesture recognition.