29,418 research outputs found
Hand Gestures for the Human-Car Interaction: the Briareo dataset
Natural User Interfaces can be an effective way to reduce driver's inattention during the driving activity. To this end, in this paper we propose a new dataset, called Briareo, specifically collected for the hand gesture recognition task in the automotive context.
The dataset is acquired from an innovative point of view, exploiting different kinds of cameras, i.e. RGB, infrared stereo, and depth, that provide various types of images and 3D hand joints.
Moreover, the dataset contains a significant amount of hand gesture samples, performed by several subjects, allowing the use of deep learning-based approaches.
Finally, a framework for hand gesture segmentation and classification is presented, exploiting a method introduced to assess the quality of the proposed dataset
Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake Expression Prediction
Frame-level visual features are generally aggregated in time with the
techniques such as LSTM, Fisher Vectors, NetVLAD etc. to produce a robust
video-level representation. We here introduce a learnable aggregation technique
whose primary objective is to retain short-time temporal structure between
frame-level features and their spatial interdependencies in the representation.
Also, it can be easily adapted to the cases where there have very scarce
training samples. We evaluate the method on a real-fake expression prediction
dataset to demonstrate its superiority. Our method obtains 65% score on the
test dataset in the official MAP evaluation and there is only one misclassified
decision with the best reported result in the Chalearn Challenge (i.e. 66:7%) .
Lastly, we believe that this method can be extended to different problems such
as action/event recognition in future.Comment: Submitted to International Conference on Computer Vision Workshop
- …