A Deep Learning Framework for Recognizing Both Static and Dynamic Gestures

A Deep Learning Framework for Recognizing Both Static and Dynamic Gestures

Mazhar, O. (TU Delft Learning & Autonomous Control)
Ramdani, Sofiane (Université de Montpellier)
Cherubini, Andrea (Université de Montpellier)

2021

Intuitive user interfaces are indispensable to interact with the human centric smart environments. In this paper, we propose a unified framework that recognizes both static and dynamic gestures, using simple RGB vision (without depth sensing). This feature makes it suitable for inexpensive human-robot interaction in social or industrial settings. We employ a pose-driven spatial attention strategy, which guides our proposed Static and Dynamic gestures Network—StaDNet. From the image of the human upper body, we estimate his/her depth, along with the region-of-interest around his/her hands. The Convolutional Neural Network (CNN) in StaDNet is fine-tuned on a background-substituted hand gestures dataset. It is utilized to detect 10 static gestures for each hand as well as to obtain the hand image-embeddings. These are subsequently fused with the augmented pose vector and then passed to the stacked Long Short-Term Memory blocks. Thus, human-centred frame-wise information from the augmented pose vector and from the left/right hands image-embeddings are aggregated in time to predict the dynamic gestures of the performing person. In a number of experiments, we show that the proposed approach surpasses the state-of-the-art results on the large-scale Chalearn 2016 dataset. Moreover, we transfer the knowledge learned through the proposed methodology to the Praxis gestures dataset, and the obtained results also outscore the state-of-the-art on this dataset.

gestures recognition
operator interfaces
human activity recognition
commercial robots and applications
cyber-physical systems

http://resolver.tudelft.nl/uuid:046e17ca-964e-498a-9212-7f4d7945674b

https://doi.org/10.3390/s21062227

1424-8220

Sensors, 21 (6)

Institutional Repository

journal article

© 2021 O. Mazhar, Sofiane Ramdani, Andrea Cherubini