Eye tracking is a process of measuring eye movement, determining where the person’s gaze is directed.
The result of an eye tracking study is an Attention Heatmap that shows how a group of participants viewed an image or video. Such eye movement data was needed to train our deep learning algorithms.
The Attention Insight algorithm is trained with approximately 5.5+ million fixations and 550+ million gaze points from eye tracking studies. The image datasets we use are both open-source and proprietary.
Data set statistics:
Our heatmaps are generated by a deep learning algorithm called Convolutional Neural Network (CNN). It is a computing system that has an architecture inspired by the biological brain and mimics how neuron layers work.
A CNN consists of multiple layers of nodes that are connected with different weight connections. These weights determine how much one node impacts the following node.
Before the training, a CNN has random weight connections resulting in inaccurate heatmaps. The difference between the generated heatmap and the “ground truth” (actual eye tracking heatmaps) is called the error. During the many training cycles, these weights between layers are adjusted so that the error is reduced. After the training, our generated heatmaps match the “ground truth” closely.
We regularly add new data sets to make sure that our algorithm meets industry standards and stays the most accurate algorithm in the market.
To measure the accuracy of our generated heatmaps, we submitted our results to the Massachusetts Institute of Technology (MIT) / Tuebingen saliency benchmark.
They sent us 300 test images, and we sent them back our model results on their testing data set of those 300 images.
After evaluating our results, MIT scientists concluded that our heatmaps match actual eye tracking heatmaps with 92.5% accuracy for general images. Across all types of designs, our heatmap accuracy is up to 96%
1 credit is charged for:
1 image or URL analysed (up to 3 viewports). Additional charge for longer images: +1 credit for each additional 3 viewports.
1 separate viewport analysis in a long image (when you select the viewport area and click ‘Analyse’).
1 AI recommendation request.
1 sec. of video analysed.
4 API transactions.
Using add-ons (plugins) will consume credits the same way as in the Web app.
1 credit is charged for:
1 image or URL analysed (up to 3 viewports). Additional charge for longer images: +1 credit for each additional 3 viewports.
1 separate viewport analysis in a long image (when you select the viewport area and click ‘Analyse’).
1 AI recommendation request.
1 sec. of video analysed.
4 API transactions.
Using add-ons (plugins) will consume credits the same way as in the Web app.
1 credit is charged for:
1 image or URL analysed (up to 3 viewports). Additional charge for longer images: +1 credit for each additional 3 viewports.
1 separate viewport analysis in a long image (when you select the viewport area and click ‘Analyse’).
1 AI recommendation request.
1 sec. of video analysed.
4 API transactions.
Using add-ons (plugins) will consume credits the same way as in the Web app.
