A probabilistic approach for human everyday activities recognition using body motion from RGB-D images

In this work, we propose an approach that relies on cues from depth perception from RGB-D images, where features related to human body motion (3D skeleton features) are used on multiple learning classifiers in order to recognize human activities on a benchmark dataset. A Dynamic Bayesian Mixture Model (DBMM) is designed to combine multiple classifier likelihoods into a single form, assigning weights (by an uncertainty measure) to counterbalance the likelihoods as a posterior probability. Temporal information is incorporated in the DBMM by means of prior probabilities, taking into consideration previous probabilistic inference to reinforce current-frame classification. The publicly available Cornell Activity Dataset [1] with 12 different human activities was used to evaluate the proposed approach. Reported results on testing dataset show that our approach overcomes state of the art methods in terms of precision, recall and overall accuracy. The developed work allows the use of activities classification for applications where the human behaviour recognition is important, such as human-robot interaction, assisted living for elderly care, among others.