Gaze Tracking in 3D Space with a Convolution Neural Network 'See What I See'
| Autor: | Hirohiko Niioka, Satoshi Asatani, Jun Miyake, Amalia Istiqlali Adiba, Seiichi Tagawa |
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| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science business.industry Pooling Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Convolutional neural network Gaze 020901 industrial engineering & automation Planar 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Cluster analysis Classifier (UML) |
| Zdroj: | AIPR |
| DOI: | 10.1109/aipr.2017.8457962 |
| Popis: | This paper presents integrated architecture to estimate gaze vectors under unrestricted head motions. Since previous approaches focused on estimating gaze toward a small planar screen, calibration is needed prior to use. With a Kinect device, we develop a method that relies on depth sensing to obtain robust and accurate head pose tracking and obtain the eye-in-head gaze direction information by training the visual data from eye images with a Neural Network (NN) model. Our model uses a Convolution Neural Network (CNN) that has five layers: two sets of convolution-pooling pairs and a fully connected-output layer. The filters are taken from the random patches of the images in an unsupervised way by k-means clustering. The learned filters are fed to a convolution layer, each of which is followed by a pooling layer, to reduce the resolution of the feature map and the sensitivity of the output to the shifts and the distortions. In the end, fully connected layers can be used as a classifier with a feed-forward-based process to obtain the weight. We reconstruct the gaze vectors from a set of head and eye pose orientations. The results of this approach suggest that the gaze estimation error is 5 degrees. This model is more accurate than a simple NN and an adaptive linear regression (ALR) approach. |
| Databáze: | OpenAIRE |
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