TY - DATA
T1 - EgoRoutine: Topic modelling for routine discovery from egocentric photo-streams
PY - 2021/09/10
AU - Estefanía Talavera Martínez
AU - Petia Radeva
AU - Nicolai Petkov
UR - https://data.4tu.nl/articles/dataset/EgoRoutine_Topic_modelling_for_routine_discovery_from_egocentric_photo-streams/16577627/1
DO - 10.4121/16577627.v1
KW - lifelogging
KW - egocentric perception
KW - behaviour analysis
KW - routine discovery
KW - pattern recognition
KW - egocentric dataset
KW - human behaviour
N2 - Developing tools to understand and visualize lifestyle is of high interest when addressing the improvement of habits and well-being of people. Routine, defined as the usual things that a person does daily, helps describe the individuals’ lifestyle. With this paper, we are the first ones to address the development of novel tools for automatic discovery of routine days of an individual from his/her egocentric images. In the proposed model, sequences of images are firstly characterized by semantic labels detected by pre-trained CNNs. Then, these features are organized in temporal-semantic documents to later be embedded into a topic models space. Finally, Dynamic-Time-Warping and Spectral-Clustering methods are used for final day routine/non-routine discrimination. Moreover, we introduce a new EgoRoutine-dataset, a collection of 104 egocentric days with more than 100.000 images recorded by 7 users. Results show that routine can be discovered and behavioural patterns can be observed.