In this paper we present an online approach to human ac-

tivity classification based on Online Growing Neural Gas (OGNG). In contrast to state-of-the-art approaches that perform training in an offline fashion, our approach is online in the sense that it circumvents the need

to store any training examples, processing the data on the fly and in one pass. The approach is thus particularly suitable in life-long learning settings where never-ending streams of data arise. We propose an archi-

tecture that consists of two layers, allowing the storage of human actions in a more memory efficient structure. While the first layer (feature map) dynamically clusters Space-Time Interest Points (STIP) and serves as basis for the creation of histogram-based signatures of human actions,

the second layer (class map) builds a classification model that relies on these human action signatures. We present experimental results on the KTH activity dataset showing that our approach has comparable per- formance to a Support Vector Machine (SVM) while performing online and avoiding to store examples explicitly.