Abstract
No one refutes the importance of datasets in the development of any new approach. Despite their importance, open access datasets in computer vision remain insufficient for some applications. This paper introduces a FallFree*, new and rich dataset that can be used for the evaluation/development of computer vision-based applications pertinent to people who use a cane as a mobility aid, e.g., fall detection, activity recognition. In particular, the FallFree dataset includes video streams captured with Kinect which offers a wide range of visual information. It is organized hierarchically, in terms of scenarios each of which is structured in terms of its features. The current FallFree dataset version covers all fall scenarios of the cane users along with various non-fall scenarios grouped into one set. Each scenario is represented through a rich set of features that can be extracted from Kinect. To widen its usability, the dataset was constructed while accounting for existing datasets' organization, size, scope, streams, types and hypotheses.