A survey of activity recognition in egocentric lifelogging datasets

With the appearance of many devices that everyday captured a large number of images. The rapid access to these huge collections of images and automatically characterizing an activity or an experience from this huge collection of unlabeled and unstructured egocentric data presents major challenges and requires novel and efficient algorithmic solutions. One of the big challenges of egocentric vision and lifelogging is to develop automatic algorithms to automatically characterize everyday activities. Such information is of high interest to predict migraines attacks or assure healthy behavior of patients and individuals of high healthy risk. In this work, we first conduct a comprehensive survey of existing egocentric datasets and we will present our future contribution to automatically characterize everyday activities.