We address the problem of recognizing actions in reallife videos. Space-time interest point-based approaches have been widely prevalent towards solving this problem. In contrast, more spatially extended features such as regions have not been so popular. The reason is, any local region based approach requires the motion flow information for a specific region to be collated temporally. This is challenging as the local regions are deformable and not well delineated from the surroundings. In this paper we address this issue by using robust tracking of regions and we show that it is possible to obtain region descriptors for classification of actions. This paper lays the groundwork for further investigation into region based approaches. Through this paper we make the following contributions a) We advocate identification of salient regions based on motion segmentation b) We adopt a state-of-the art tracker for robust tracking of the identified regions rather than using isolated space-time blocks c) We propose optical flow based region descriptors to encode the extracted trajectories in piece-wise blocks. We demonstrate the performance of our system on real-world data sets.