A four dukkha state-space model for hand tracking

Abstract

In this paper, we propose a hand tracking method which was inspired by the notion of the four dukkha: birth, aging, sickness and death (BASD) in Buddhism. Based on this philosophy, we formalize the hand tracking problem in the BASD framework, and apply it to hand track hand gestures in isolated sign language videos. The proposed BASD method is a novel nature-inspired computational intelligence method which is able to handle complex real-world tracking problem. The proposed BASD framework operates in a manner similar to a standard state-space model, but maintains multiple hypotheses and integrates hypothesis update and propagation mechanisms that resemble the effect of BASD. The survival of the hypothesis relies upon the strength, aging and sickness of existing hypotheses, and new hypotheses are birthed by the fittest pairs of parent hypotheses. These properties resolve the sample impoverishment problem of the particle filter. The estimated hand trajectories show promising results for the American sign language.

HGR-ViT: Hand Gesture Recognition with Vision Transformer

“…In general, there are two categories of hand gesture recognition: static hand gesture recognition and dynamic hand gesture recognition. Static hand gesture recognition [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ] involves interpreting hand gestures in a stationary position, resulting in higher accuracy due to less susceptibility to environmental factors. On the other hand, dynamic hand gesture recognition [ 9 , 10 , 11 , 12 , 13 ] recognises gestures with complex movements and temporal dynamics, which are more intuitive, natural, and versatile.…”

SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition

“…HGR can be broadly categorized into two types: static HGR [1][2][3][4][5][6][7][8] and dynamic HGR [9][10][11][12][13]. The former involves classifying hand gestures from images, while the latter involves classifying hand gestures from videos.…”