Gesture recognition is a feature in human-machine interaction that allows more natural interaction without the use of complex devices. For this reason, several methods of gesture recognition have been developed in recent years. However, most real time methods are designed to operate on a Personal Computer with high computing resources and memory. In this paper, we analyze relevant methods found in the literature in order to investigate the ability of smart camera to execute gesture recognition algorithms. We elaborate two hand gesture recognition pipelines. The first method is based on invariant moments extraction and the second on finger tips detection. The hand detection method used for both pipeline is based on skin color segmentation. The results obtained show that the un-optimized versions of invariant moments method and finger tips detection method can reach 10 fps on embedded processor and use about 200 kB of memory.