Hand Gesture Recognition in Real Time for Automotive Interfaces: A Multimodal Vision-Based Approach and Evaluations

By Eshed Ohn-Bar, Mohan Manubhai Trivedi. IEEE transactions on intelligent transportation systems (Print), 2014

443 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/8fdb23b1de6fcac695d9746d30e572556cf1e665

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In Computer Vision and Pattern Recognition

In Human-Computer Interaction

Citations in the corpus

Depth-Based Hand Pose Estimation: Methods, Data, and Challenges (2015, unknown): “Human hand pose estimation empowers many practical applications, for example sign language recognition [14], visual interfaces [17], and driver analysis [20].”
A Low Power, Fully Event-Based Gesture Recognition System (2017, methodology): “Multimodal systems [46, 31, 33, 15] have also been used to improve performance.”
Challenges in multimodal gesture recognition (2016, background): “Applications HRI ((Lee, 2006)), HCI (Malik and Laszlo, 2004), HMI (Ohn-Bar and Trivedi, 2014), sign language (Starner and Pentland, 1998), gesture to speech (Fels, 1994), presentations (Licsár and Szirányi, 2004), virtual environments (Wexelblat, 1995), 3d modeling, multimodal interaction (Rauschert”
EgoGesture: A New Dataset and Benchmark for Egocentric Hand Gesture Recognition (2018, background): “[15] E. Ohn-Bar and M. M. Trivedi, “Hand gesture recognition in real time for automotive interfaces: A multimodal vision-based approach and evaluations,” IEEE Trans.”
Hand gesture recognition with 3D convolutional neural networks (2015, methodology): “00 ©2015 IEEE 1 crafted spatio-temporal features and classifiers for in-car hand-gesture recognition with RGBD data [14].”
Multi-Modality Fusion based on Consensus-Voting and 3D Convolution for Isolated Gesture Recognition (2016, unknown): “Ohn-Bar et al [20] ﬁrst detects a hand in the region of interaction and then combines RGB and depth descriptor for classiﬁcation.”
Vision-Based Driver Assistance Systems: Survey, Taxonomy and Advances (2015, background): “The purpose of these systems is typically for monitoring the occupants of the car (the driver in particular), providing functions such as driver monitoring (DM), back-seat child monitoring [13] and gesture recognition [14].”
MMTM: Multimodal Transfer Module for CNN Fusion (2019, unknown): “HOG+HOG2 [64] RGB+Depth 36.”
Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3D Convolutional Neural Networks (2016, methodology): “, [21, 23, 25, 27], regards gesture segmentation and classification separately.”
Computer Vision and Image Understanding Computer Vision for Assistive Technologies (unknown): “…combining RGB and depth descriptors: they have been already successfully used to classify hand gestures for a human-machine interface application in the car ( Ohn-Bar and Trivedi, 2014 ) or to describe the articulated human body that has a large number of kinematic joints ( Wang et al., 2014a ).”
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations (2017, methodology): “Having multiple data streams has led to the study of combining different sources of information such as depth and pose [4, 40, 52, 69], color and pose [88], and all of them [19].”