HAck: A system for the recognition of human actions by kernels of visual strings

In this paper we propose HAcK, a novel method for recognizing Human Actions by string Kernel; the main idea is to represent each action through a sequence of visual characters, namely a string, able to model the temporal evolution of the events. Visual characters are extracted by analyzing global descriptors of the scene and by taking advantage on the depth information provided by a Kinect sensor. The similarity between actions is evaluated with a fast global alignment kernel, which allows to deal with actions of different length as well as with the noise introduced during the features extraction step. HAcK has been evaluated over two standard datasets and the obtained results, compared with state of the art approaches, confirm its effectiveness and its applicability in real environments.