Long-Distance Gesture Recognition using Dynamic Neural Networks

TL;DR

This paper introduces a dynamic neural network approach for long-distance gesture recognition, improving accuracy and efficiency over existing methods, especially in resource-constrained scenarios and for distant interactions.

Contribution

It presents a novel dynamic neural network that selectively processes spatial regions of input data, enhancing long-distance gesture recognition performance and computational efficiency.

Findings

Outperforms previous state-of-the-art on LD-ConGR dataset

Achieves higher recognition accuracy for long-distance gestures

Demonstrates improved compute efficiency in resource-limited settings

Abstract

Gestures form an important medium of communication between humans and machines. An overwhelming majority of existing gesture recognition methods are tailored to a scenario where humans and machines are located very close to each other. This short-distance assumption does not hold true for several types of interactions, for example gesture-based interactions with a floor cleaning robot or with a drone. Methods made for short-distance recognition are unable to perform well on long-distance recognition due to gestures occupying only a small portion of the input data. Their performance is especially worse in resource constrained settings where they are not able to effectively focus their limited compute on the gesturing subject. We propose a novel, accurate and efficient method for the recognition of gestures from longer distances. It uses a dynamic neural network to select features from gesture-containing spatial regions of the input sensor data for further processing. This helps the network focus on features important for gesture recognition while discarding background features early on, thus making it more compute efficient compared to other techniques. We demonstrate the performance of our method on the LD-ConGR long-distance dataset where it outperforms previous state-of-the-art methods on recognition accuracy and compute efficiency.