Fusing depth and colour information for human action recognition

In recent years, human action recognition systems have been increasingly developed to support a wide range of application areas, such as surveillance, behaviour analysis, security, and many others. In particular, data fusion approaches that use depth and colour information (i.e., RGB-D data) seem to be particularly promising for recognizing large classes of human actions with a high level of accuracy. Anyway, existing data fusion approaches are mainly based on feature fusion strategies, which tend to suffer of some limitations, including the difficult of combining different feature types and the management of missing information. To address the two problems just reported, we propose an RGB-D data based human action recognition system supported by a decision fusion strategy. The system, starting from the well-known Joint Directors of Laboratories (JDL) data fusion model, analyses human actions separately for each channel (i.e., depth and colour). The actions are modelled as a sum of visual words by using the traditional Bag-of-Visual-Words (BoVW) model. Subsequently, on each channel, these actions are classified by using a multi-class Support Vector Machine (SVM) classifier. Finally, the classification results are fused by a Naive Bayes Combination (NBC) method. The effectiveness of the proposed system has been proven on the basis of three public datasets: UTKinect-Action3D, CAD-60, and LIRIS Human Activities. Experimental results, compared with key works of the current state-of-the-art, have shown that what we propose can be considered a concrete contribute to the action recognition field.