Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks

Joshi, Ajjen; Ghosh, Soumya; Betke, Margrit; Sclaroff, Stan; Pfister, Hanspeter

doi:10.1109/cvpr.2017.56

Cited by 16 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dynamic hand gesture recognition relies on learning temporal (i.e., trajectory, speed) and spatial (i.e., hand shape and location) features for a gesture. There are many techniques that have been used for classification, such as template-based approach [3], [4], statistical methods (Hidden Markov Analysis, Conditional Random Field, and causal analysis) [5]- [8].…”

Section: Related Workmentioning

confidence: 99%

Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks

Obaid

Babadi

Yoosofan

2020

Applied Computer Systems

View full text Add to dashboard Cite

Deep learning is a new branch of machine learning, which is widely used by researchers in a lot of artificial intelligence applications, including signal processing and computer vision. The present research investigates the use of deep learning to solve the hand gesture recognition (HGR) problem and proposes two models using deep learning architecture. The first model comprises a convolutional neural network (CNN) and a recurrent neural network with a long short-term memory (RNN-LSTM). The accuracy of model achieves up to 82 % when fed by colour channel, and 89 % when fed by depth channel. The second model comprises two parallel convolutional neural networks, which are merged by a merge layer, and a recurrent neural network with a long short-term memory fed by RGB-D. The accuracy of the latest model achieves up to 93 %.

show abstract

Section: Related Workmentioning

confidence: 99%

Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks

Obaid

Babadi

Yoosofan

2020

Applied Computer Systems

View full text Add to dashboard Cite

show abstract

“…Personalization: Personalized training has been applied to other domains (e.g. facial action unit [10] and gesture recognition [72,27]) but not yet on vehicular glance classification. In the context of eye tracking, personalization is usually achieved through apriori user calibration.…”

Section: Related Workmentioning

confidence: 99%

Driver Glance Classification In-the-wild: Towards Generalization Across Domains and Subjects

Banerjee¹,

Joshi²,

Turcot³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Distracted drivers are dangerous drivers. Equipping advanced driver assistance systems (ADAS) with the ability to detect driver distraction can help prevent accidents and improve driver safety. In order to detect driver distraction, an ADAS must be able to monitor their visual attention. We propose a model that takes as input a patch of the driver's face along with a crop of the eye-region and classifies their glance into 6 coarse regions-of-interest (ROIs) in the vehicle. We demonstrate that an hourglass network, trained with an additional reconstruction loss, allows the model to learn stronger contextual feature representations than a traditional encoder-only classification module. To make the system robust to subject-specific variations in appearance and behavior, we design a personalized hourglass model tuned with an auxiliary input representing the driver's baseline glance behavior. Finally, we present a weakly supervised multi-domain training regimen that enables the hourglass to jointly learn representations from different domains (varying in camera type, angle), utilizing unlabeled samples and thereby reducing annotation cost.

show abstract

“…However, their model does not achieve the goal of extracting higher-level features of hands. Also [14] have focused on using Hierarchical Bayesian Neural Networks and active learning to personalize the human gestures.…”

Section: Related Workmentioning

confidence: 99%