Histogram of oriented velocities for eye movement detection

Research in various fields including psychology, cognition, and medical science deal with eye tracking data to extract information about the intention and cognitive state of a subject. For the extraction of this information, the detection of eye movement types is an important task. Modern eye tracking data is noisy and most of the state-ofthe-art algorithms are not developed for all types of eye movements since they are still under research. We propose a novel feature for eye movement detection, which is called histogram of oriented velocities. The construction of the feature is similar to the well known histogram of oriented gradients from computer vision. Since the detector is trained using machine learning, it can always be extended to new eye movement types. We evaluate our feature against the state-of-the-art on publicly available data. The evaluation includes different machine learning approaches such as support vector machines, regression trees, and k nearest neighbors. We evaluate our feature together with the machine learning approaches for different parameter sets. We provide a matlab script for the computation and evaluation as well as an integration in EyeTrace which can be downloaded at http://www.ti.uni-tuebingen.de/Eyetrace.1751.0.html.