Ways of improving the precision of eye tracking data: Controlling the influence of dirt and dust on pupil detection

Eye-tracking technology has to date been primarily employed in research. With recent advances in aordable video-based devices, the implementation of gaze-aware smartphones, and marketable driver monitoring systems, a considerable step towards pervasive eye-tracking has been made. However, several new challenges arise with the usage of eye-tracking in the wild and will need to be tackled to increase the acceptance of this technology. The main challenge is still related to the usage of eye-tracking together with eyeglasses, which in combination with reflections for changing illumination conditions will make a subject "untrackable". If we really want to bring the technology to the consumer, we cannot simply exclude 30% of the population as potential users only because they wear eyeglasses, nor can we make them clean their glasses and the device regularly. Instead, the pupil detection algorithms need to be made robust to potential sources of noise. We hypothesize that the amount of dust and dirt on the eyeglasses and the eye-tracker camera has a significant influence on the performance of currently available pupil detection algorithms. Therefore, in this work, we present a systematic study of the eect of dust and dirt on the pupil detection by simulating various quantities of dirt and dust on eyeglasses. Our results show 1) an overall high robustness to dust in an o-focus layer. 2) the vulnerability of edge-based methods to even small in-focus dust particles. 3) a trade-o between tolerated particle size and particle amount, where a small number of rather large particles showed only a minor performance impact

Artificial Intelligence based Position Detection for Hydraulic Cylinders using Scattering Parameters

Position detection of hydraulic cylinder pistons is crucial for numerous industrial automation applications. A typical traditional method is to excite electromagnetic waves in the cylinder structure and analytically solve the piston position based on the scattering parameters measured by a sensor. The core of this approach is a physical model that mathematically describes the relationship between the measured scattering parameters and the targeted piston position. However, this physical model has shortcomings in accuracy and adaptability, especially in extreme conditions. To overcome this problem, we propose Artificial Intelligence (AI)-based methods to learn the relationship directly data-driven. As a result, all Artificial Neural Network (ANN) models in this paper consistently outperform the physical one by a large margin. Given the success of AI-based models for our task, we further deliberate the choice of models based on domain knowledge and provide in-depth analyses combining model performance with the physical characteristics. Specifically, we use Convolutional Neural Network (CNN)s to discover local interactions of input among adjacent frequencies, apply Complex-Valued Neural Network (CVNN) to exploit the complex-valued nature of electromagnetic scattering parameters, and introduce a novel technique named Frequency Encoding to add weighted frequency information to the model input. By combining these three techniques, our best performing model, a complex-valued CNN with Frequency Encoding, manages to significantly reduce the test error to hardly 1/12 of the one given by the traditional physical model.Comment: 16 pages, 10 figure