This work investigates some visual functionalities required in Human-Robot Interaction (HRI) to evaluate the intention of a person to interact with another agent (robot or human). Analyzing the upper part of the human body which includes the head and the shoulders, we obtain essential cues on the person's intention. We propose a fast and efficient upper body detector and an approach to estimate the upper body pose in 2D images. The upper body detector derived from a state-of-the-art pedestrian detector identifies people using Aggregated Channel Features (ACF) and fast feature pyramid whereas the upper body pose classifier uses a sparse representation technique to recognize their shoulder orientation. The proposed detector exhibits state-of-the-art result on a public dataset in terms of both detection performance and frame rate. We also present an evaluation of different feature set combinations for pose classification using upper body images and report promising results despite the associated challenges.