The MoCA dataset, kinematic and multi-view visual streams of fine-grained cooking actions

MoCA is a bi-modal dataset in which we collect Motion Capture data and video sequences acquired from multiple views, including an ego-like viewpoint, of upper body actions in a cooking scenario. It has been collected with the specific purpose of investigating view-invariant action properties in both biological and artificial systems. Besides that, it represents an ideal test bed for research in a number of fields – including cognitive science and artificial vision – and application domains – as motor control and robotics. Compared to other benchmarks available, MoCA provides a unique compromise for research communities leveraging very different approaches to data gathering: from one extreme of action recognition in the wild – the standard practice nowadays in the fields of Computer Vision and Machine Learning – to motion analysis in very controlled scenarios – as for motor control in biomedical applications. In this work we introduce the dataset and its peculiarities, and discuss a baseline analysis as well as examples of applications for which the dataset is well suited.