Online Gesture Recognition Algorithm Applied to HUD Based Smart Driving System

Wang, Jingyao; Chen, Jing; Qiao, Yuanyuan; Zhou, Junyan; Wang, Yongtian

doi:10.1109/ismar-adjunct.2019.00-26

Cited by 6 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chalasani et al [7] generated augmented data by using a green screen to capture hand gestures and then overlaid these on different backgrounds. Xie et al [49] performed simultaneous detection of hands and tracking of keypoints to support gesture recognition while Wang et al [47] examined online gesture recognition for interaction with a vehicle HUD. However, all these prior studies use image data.…”

Section: Online Gesture Recognitionmentioning

confidence: 99%

Gesture Spotter: A Rapid Prototyping Tool for Key Gesture Spotting in Virtual and Augmented Reality Applications

Shen

Dudley

et al. 2022

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

In this paper we examine the task of key gesture spotting: accurate and timely online recognition of hand gestures. We specifically seek to address two key challenges faced by developers when integrating key gesture spotting functionality into their applications. These are: i) achieving high accuracy and zero or negative activation lag with single-time activation; and ii) avoiding the requirement for deep domain expertise in machine learning. We address the first challenge by proposing a key gesture spotting architecture consisting of a novel gesture classifier model and a novel single-time activation algorithm. This key gesture spotting architecture was evaluated on four separate hand skeleton gesture datasets, and achieved high recognition accuracy with early detection. We address the second challenge by encapsulating different data processing and augmentation strategies, as well as the proposed key gesture spotting architecture, into a graphical user interface and an application programming interface. Two user studies demonstrate that developers are able to efficiently construct custom recognizers using both the graphical user interface and the application programming interface.

show abstract

Section: Online Gesture Recognitionmentioning

confidence: 99%